Chapter 1
Cognitive Radio
1.1 Introduction
Cognitive wireless [1] is an a promising technology that can be utilized to enhance the utilization efficiency of the wireless spectrum, by permitting secondary user (SU) webs to co-exist alongside Primary user (PU) webs across spectrum sharing. A key necessity is that SU transmission will not adversely alter the PUs’ performance. To accomplish this, a public method involves the SUs early noticing if at least one PU is sending, that is usually denoted to as ‘spectrum sensing’. If no signals are noticed, the SUs are allowed to transmit. The significance of gesture detection can be perceived by its inclusion in the IEEE 802.22 [2] standard; a average crafted on cognitive wireless methods.
Signal detection has been extensively investigated above the past insufficient decades, inside disparate contexts of request , Inspired by a little of those seminal works, a number of gesture detection examinations have been counseled to notice PU transmission after there are several accord antennas at the SUs. Optimality is frequently believed in the Neyman-Pearson sense that involves contrasting the generalized likelihood ratio (GLR) [3] to a user-designed detection threshold. The GLR can be utilized to ascertain the fake alarm and detection probabilities that can next be afterward utilized to design the threshold. The particular form of the GLR is reliant on the number of PU transmission signals, and whether sounds and/or channel data is recognized at the SU receiver giving the gesture detection.
A reasonable scenario is to accept that nothing is recognized at the SU receiver, i.e., no sound and channel data are known. For this scenario, the fake alarm and detection probability have been analyzed after there is merely one PU signal. Though, the simultaneous attendance of several PU signals is a public occurrence in present and subsequent creation systems. This could transpire, for example, in arrangements whereas spatial multiplexing methods are retained, or whereas disparate PUs transmits simultaneously. Furthermore, the number of PU signals is clearly anticipated to produce as evidenced by the anticipated use of large antenna arrays and by the trend towards extra dense and heterogeneous networks. As a consequence, we frequently encounter scenarios whereas the number of PU signals is no less than the number of receives antennas.
Fig 1.1 Architecture of the cognitive networks.
1.2 Challenges in Cognitive Radio
There are various challenges of Cognitive Radio which are explained below [3]:
Hardware Requirements
Spectrum detecting for cognitive wireless requests needs elevated sampling rate, elevated resolution analog to digital converters (ADCs) alongside colossal vibrant scope, and elevated speed gesture processors. Sound variance estimation methods have been popularly utilized for optimal receiver sketches like channel estimation, soft data creation etc., as well as for enhanced handoff, manipulation domination, and channel allocation techniques.
Hidden Main User Problem
The hidden main user setback is comparable to the hidden node setback in Messenger Sense Several Accessing (CSMA). It can be provoked by countless factors encompassing harsh multipath disappearing or shadowing noted by secondary users as scanning for main users’ transmissions. Fig. 2 displays illustration of the hidden node setback whereas the dashed circles display the working scopes of the main user and the cognitive wireless device. Here, cognitive wireless mechanism reasons unwanted interference to the main user as the main transmitter’s gesture might not be noticed because of the locations of devices.
Fig 1.2 Illustration of hidden primary user problem in cognitive radio systems.
Detecting Range Spectrum Main Users
For commercially obtainable mechanisms, there are two main kinds of technologies: fixed frequency and range spectrum. The two main range spectrum technologies are frequency hopping spread-spectrum (FHSS) and direct-sequence range spectrum (DSSS) [5]. Fixed frequency mechanisms work at a solitary frequency or channel. An example to such arrangements is IEEE 802.11a/g established WLAN. FHSS mechanisms change their operational frequencies vibrantly to several narrowband channels. This is recognized as hopping and gave according to a sequence that is recognized by both transmitter and receiver. DSSS mechanisms are comparable to FHSS devices; though, they use a solitary group to range their energy.
Sensing Duration and Frequency
Primary users can claim their frequency groups anytime as cognitive wireless is working on their bands. In order to stop interference to and from main license proprietors, cognitive wireless ought to be able to recognize the attendance of main users as swiftly as probable and ought to vacate the group immediately. Hence, detecting methods ought to be able to recognize the attendance of main users inside precise duration. This necessity poses a check on the presentation of detecting algorithm and creates a trial for cognitive wireless design.
Decision mixture in Obliging Sensing
In the case of obliging detecting, allocating data amid cognitive radios and joining aftermath from assorted measurements is a challenging task. The public data can be soft or hard decisions made by every single cognitive device. On the supplementary hand, hard-decisions are discovered to present as good as soft decisions after the number of cooperating users is high. The optimum mixture law for joining detecting data is the Chair-Varshney law that is established on log-likelihood ratio examination. Likelihood ratio examinations are utilized for making association employing decisions from secondary users. Various, simpler, methods for joining detecting aftermath are employed. The presentations of equal gain joining (EGC), selection joining (SC), and switch and stay joining (SSC) are investigated for power detector established spectrum detecting below Rayleigh fading.
1.3 The spectrum scarcity problem
The entire spectrum has been fully allocated, leaving very little space for additional wireless services. In addition, the current spectrum policy makes it difficult for the rapid deployment of new services, which is particularly crucial for emergency services.
Moreover, it has been found that the spectrum is underutilized temporally, spatially and spectrally. This has lead to the creation of large portions of underutilized and vacant spectrum, which are termed as spectrum white spaces or spectrum holes. For instance, the dynamic nature of the spectrum utilization by mobile telephony services has resulted in an inefficient usage of the spectrum temporally. In the television and FM broadcast bands, buffer spaces have been created in order to maintain safe distances between broadcasting stations operating on the same frequency channel. In addition, guard bands have been assigned between adjacent station frequency channels in order to avoid adjacent channel interference.
Furthermore, the demand for wireless services has been steadily increasing due to the growing need for wireless broadband connectivity. There has been an increase in the number of users of wireless services, as well as new wireless services that are constantly evolving. Moreover, the requirements of federal agencies and emergency services place high and uncompromising constraints on the spectrum. The current regulatory structure does not possess the flexibility to allow the dynamic reuse of the licensed spectrum even when it is idle as well as fast deployment of new wireless services. This rigid spectrum management system is the main cause for a potential spectrum scarcity in the near future.
The phenomenal advancements in wireless telecommunication technology in congruence with the growth of the internet has given rise to advanced applications with higher data rates, thus rendering the radio spectrum as an extremely scarce resource. However, spectrum measurement studies conducted in revealed the underutilization of this radio spectrum in both spatial and temporal domains. This underutilization presented a serious challenge for the government and national
spectrum regulating bodies responsible for regulating the spectrum to satisfy the ever increasing demand for mobile connectivity. With the goal of efficient, intelligent and fair use of this scarce but expensive radio spectrum, the Federal Communications Commission (FCC) issued the Spectrum Policy Task Force (SPTF) report. The SPTF report suggested the replacement of the current rigid ‘command and control’ spectrum policies with new flexible spectrum policies under the Dynamic Spectrum Access (DSA) paradigm. This DSA paradigm provided a tremendous impetus for research in the development of flexible spectrum policies for the efficient utilization of the radio spectrum, by allowing unsubscribed Secondary User (SU) access. This chapter introduces the DSA techniques and presents the rationale for DSA in infrastructure based networks considered in this thesis. This chapter is organized
1.1. Solutions to the Spectrum Scarcity Problem
There have been substantial research efforts aimed at improving the spectrum utilization. New services can be accommodated by redefining the spectrum that has been assigned to existing services that utilize the spectrum sparsely. However, in the current spectrum regime, any potential reallocation of the spectrum can have many political and commercial consequences .
In June 2002, the FCC commissioned the Spectrum Policy Task Force (SPTF), which is a body that makes recommendations on reforming the spectrum policy. In its final report, the SPTF has suggested various methods to improve the spectrum utilization, such as exploitation of the spectrum along all its dimensions, reuse of the underutilized spectrum, and transformation of the current commandant- control regime into a more flexible market-based system.
Fig 1.3 Underutilized portions of spectrum (9kHz-1GHz band)
1.4 Introduction to Cognitive radio
Cognitive radio is a promising technology which can be used to improve the utilization efficiency of the radio spectrum, by allowing secondary user (SU) networks to co-exist with primary user (PU) networks through spectrum sharing. A key requirement is that SU transmission will not adversely affect the PUs’ performance. To achieve this, a common technique involves the SUs first detecting if at least one PU is transmitting, which is commonly referred to as ‘spectrum sensing’. If no signals are detected, the SUs are allowed to transmit. The importance of signal detection can be seen by its inclusion in the IEEE 802.22 standard; a standard built on cognitive radio techniques [1].
Signal detection has been extensively investigated over the past few decades, within different contexts of application , Inspired by some of those seminal works, a number of signal detection tests have been proposed to detect PU transmission when there are multiple receive antennas at the SUs. Optimality is often considered in the Neyman-Pearson sense, which involves comparing the generalized likelihood ratio (GLR) to a user-designed detection threshold. The GLR can be used to determine the false alarm and detection probabilities, which can then be subsequently used to design the threshold. The particular form of the GLR is dependent on the number of PU transmission signals, and whether noise and/or channel information is known at the SU receiver performing the signal detection.
A reasonable scenario is to assume that nothing is known at the SU receiver, i.e., no noise and channel information are known. For this scenario, the false alarm and detection probability have been analyzed when there is only one PU signal. However, the simultaneous presence of multiple PU signals is a common occurrence in current and next generation systems. This may occur, for example, in systems where spatial multiplexing techniques are employed, or where different PUs transmits simultaneously. Furthermore, the number of PU signals is clearly expected to grow as evidenced by the forthcoming use of massive antenna arrays and by the trend towards more dense and heterogeneous networks. As a result, we often encounter scenarios where the number of PU signals is no less than the number of receives antennas.
Fig 1.4 The n-th transmission time slot of cognitive networks [2].
1.5 Applications of Cognitive Radio
There are various applications which are related to Cognitive Radio are explained below [5]:
1.5.1 SMART GRID NETWORKS
Transformation of the 20th-centrury power grid into a Smart Grid is being promoted by many governments as a way of addressing energy independence and sustainability, global warming and emergency resilience issues. The Smart Grid is comprised of three high-level layers, from an architectural perspective: the physical power layer, the communication networking layer, and the applications layer. A Smart Grid transforms the way power is generated, delivered, consumed and billed. Adding intelligence throughout the newly networked grid increases grid reliability, improves demand handling and responsiveness, increases efficiency, better harnesses and integrates renewable/distributed energy sources, and potentially reduces costs for the provider and consumers.
1.5.2 PUBLIC SAFETY NETWORKS
Wireless communications are extensively used by emergency responders, such as police, fire and emergency medical services, to prevent or respond to incidents, and by citizens to quickly access emergency services. Public safety workers are increasingly being equipped with wireless laptops, handheld computers, and mobile video cameras to improve their efficiency, visibility, and ability to instantly collaborate with central command, coworkers and other agencies. The desired wireless services for public safety extend from voice to messaging, email, Web browsing, database access, picture transfer, video streaming, and other wideband services. Video surveillance cameras and sensors are becoming important tools to extend the eyes and ears of public safety agencies. Correspondingly, data rates, reliability and delay requirements vary from service to service.
1.5.3 CELLULAR NETWORKS
The use of cellular networks is undergoing dramatic changes in recent years, with consumer’s expectations of being always connected, anywhere and anytime. The introduction of smart phones, the popularity of social networks, growing media sites such as YouTube, Hulu, flicker, introduction of new devices such as e-readers, have all added to the already high and growing use of cellular networks for conventional data services such as email and web-browsing. This trend is also identified in the FCC’s visionary National Broadband Plan.
1.5.4 WIRELESS MEDICAL NETWORKS (MBANS)
In recent years there has been increasing interest in implementing ubiquitous monitoring of patients in hospitals for vital signs such as temperature, pressure, blood oxygen and electrocardiogram (ECG). Normally these vitals are monitored by on-body sensors that are then connected by wires to a bedside monitor. MBANS is a promising solution for eliminating these wires, thus allowing sensors to reliably and inexpensively collect multiple parameters simultaneously and relay the monitoring information wirelessly so that clinicians can respond rapidly. Introduction of MBANS for wireless patient monitoring is an essential component to improving patient outcomes and lowering healthcare costs. Through low-cost, wireless devices, universal patient monitoring can be extended to most if not all patients in many hospitals. With such ubiquitous monitoring, changes in a patient’s condition can be recognized at an early stage and appropriate action taken.
1.6 Cognitive Radio and Current Mobile Device Trends
CRs are defined to have the capabilities to scan and detect spectrum holes, making them spectrum aware, while simultaneously tracking the channel conditions, making them environment aware . The CRs are also envisioned to have policy based mechanisms for decision making based on the spectrum availability and channel state information. The ability to reconfigure their hardware and software with respect to Radio Access Technology (RAT) for the opportunistic occupancy of the white spaces in any spectrum band is also an essential requirement for the CR terminals .
To aid the idea of reconfiguration to any spectrum band, the CRs are envisioned to be based on the Software Defined Radio (SDR) platform. These SDRs are conceptualized to be implemented 80% in software and 20% in hardware, unlike current mobile devices which generically contain 80% hardware and 20% software . With different RATs implemented in software on general purpose processors, the necessary software patches with respect to a particular RAT are envisioned to be downloaded whenever required, thus providing multi-RAT (multi-mode) functionality to the SUs for enabling flexible global operation .
The current industry trend however can be seen to approach the envisioned cognitive capability and re-configurability through a different direction with smartphones supporting multiple RATs on separate chips already flooding the market1. These latest smartphones support 2G technologies such as GSM/EDGE networks in the 800, 850, 1800, 1900 MHz frequency bands as well as 3G technologies such as UMTS/HSPA in the 850, 1900, 2100 MHz frequency bands. Apart from just 2G and 3G technology support, these devices also support WiFi (IEEE 802.11) networks . Although these current smartphones are programmed for BS-subscriber master-slave operation, basic decision making capabilities coupled with spectrum scanning and RAT reconfiguration capabilities, can potentially transform these smartphones for CR-like operation in the future
1.7 Challenges in Cognitive Radio
There are various challenges of Cognitive Radio which are explained below [3]:
1.7.1 Hardware Requirements
Spectrum sensing for cognitive radio applications requires high sampling rate, high resolution analog to digital converters (ADCs) with large dynamic range, and high speed signal processors. Noise variance estimation techniques have been popularly used for optimal receiver designs like channel estimation, soft information generation etc., as well as for improved handoff, power control, and channel allocation techniques.
1.7.2 Hidden Primary User Problem
The hidden primary user problem is similar to the hidden node problem in Carrier Sense Multiple Accessing (CSMA). It can be caused by many factors including severe multipath fading or shadowing observed by secondary users while scanning for primary users’ transmissions. Fig. 2 shows illustration of the hidden node problem where the dashed circles show the operating ranges of the primary user and the cognitive radio device. Here, cognitive radio device causes unwanted interference to the primary user as the primary transmitter’s signal could not be detected because of the locations of devices.
Fig 1.5 Illustration of hidden primary user problem in cognitive radio systems.
1. Detecting Spread Spectrum Primary Users
For commercially available devices, there are two main types of technologies: fixed frequency and spread spectrum. The two major spread spectrum technologies are frequency hopping spread-spectrum (FHSS) and direct-sequence spread spectrum (DSSS). Fixed frequency devices operate at a single frequency or channel. An example to such systems is IEEE 802.11a/g based WLAN. FHSS devices change their operational frequencies dynamically to multiple narrowband channels. This is known as hopping and performed according to a sequence that is known by both transmitter and receiver. DSSS devices are similar to FHSS devices; however, they use a single band to spread their energy.
2. Sensing Duration and Frequency
Primary users can claim their frequency bands anytime while cognitive radio is operating on their bands. In order to prevent interference to and from primary license owners, cognitive radio should be able to identify the presence of primary users as quickly as possible and should vacate the band immediately. Hence, sensing methods should be able to identify the presence of primary users within certain duration. This requirement poses a limit on the performance of sensing algorithm and creates a challenge for cognitive radio design.
3. Decision Fusion in Cooperative Sensing
In the case of cooperative sensing, sharing information among cognitive radios and combining results from various measurements is a challenging task. The shared information can be soft or hard decisions made by each cognitive device. On the other hand, hard-decisions are found to perform as good as soft decisions when the number of cooperating users is high. The optimum fusion rule for combining sensing information is the Chair-Varshney rule which is based on log-likelihood ratio test . Likelihood ratio test are used for making classification using decisions from secondary users. Various, simpler, techniques for combining sensing results are employed. The performances of equal gain combining (EGC), selection combining (SC), and switch and stay combining (SSC) are investigated for energy detector based spectrum sensing under Rayleigh fading.
1.8 Spectrum Access
Traditionally, the spectrum access or MAC protocols for spectrum overlay-based CRNs are designed with the objective of maximizing the throughput of secondary users while protecting primary user from collisions due to secondary transmissions and to provide fair and efficient sharing of available spectrum among secondary users. For the RF-powered CRNs, two types of MAC protocols, i.e., fixed and random spectrum access can be adopted to achieve similar performance objectives [4].
1. Fixed spectrum access:
For this type of protocols, radio resources are statically allocated to users e.g., based on time-division multiple access [TDMA], or orthogonal frequency-division multiple access [OFDMA]. Given the availability of RF energy, the radio resource must be allocated optimally among multiple secondary users. For example, radio resource should be allocated to the users which are not harvesting RF energy e.g., out of range of transmitting RF sources. Also, the radio resource should be allocated to the users which have sufficient amount of harvested RF energy to use the allocated radio bandwidth.
2. Random spectrum access:
For this type of protocols e.g., slotted ALOHA and carrier-sense multiple access with collision avoidance [CSMA/CA], the secondary users contend for radio resources for data transmission. As in conventional CRNs, the main problem to be addressed in contention-based spectrum access is collision avoidance. However, this problem becomes more complicated due to RF energy harvesting. Firstly, secondary users have to decide whether to harvest RF energy or contend for data transmission. Secondly, to avoid collision, a back off mechanism can be applied. These decisions must consider the level of remaining energy and amount of RF energy to be harvested. For example, if the channel contention is high, some secondary users should back off their transmissions and harvest RF energy instead. This is not only beneficial for reducing collision, but also to increase the energy level. If the primary user re-occupies the channel, the secondary user may remain in the same channel but switch its mode to harvest RF energy. Note that, in the RF-powered CRNs, the secondary user needs to switch a channel not only when the primary user re-occupies the channel, but also when the secondary user needs to harvest RF energy.
3. Dynamic Spectrum Access
The concept of dynamic spectrum access is the identification of spectrum holes or white spaces and uses them to communicate. Dynamic spectrum access is the most vital application of cognitive radios. The PU bands are opportunistically accessed by the SU networks such that the interference caused to the PUs is negligible.
This is a technique by which a radio system adapts to available spectrum holes with limited spectrum use rights dynamically, in response to changing circumstances and objectives: the created interference changes the radio’s state in environmental constraints. The main task of DSA is to overcome two types of interference:
i) harmful interference caused by device malfunctioning and
ii) harmful interference caused by malicious user.
1.8.1 Classification of DSA Techniques
With smart CR like SU terminals for operation, the DSA paradigm can be explored in a variety of ways with the goal of achieving intelligent, efficient and fair utilization in the spectrum band of operation. Thus based on spectrum band of operation, DSA techniques can be classified broadly into two basic types:
‘ Licensed spectrum bands
‘ Unlicensed spectrum bands
The other type of classification for DSA techniques can be based on the network structure as shown below:
‘ Infrastructure based networks
‘ Ad-hoc networks
Within the above classification, further sub-classifications are also possible based on power levels or types of architecture. The above classifications are aimed at clearly identifying the scope and the focus of the work presented in this thesis.
1.9 Research Motivation
Opportunistic spectrum access has become a high priority research area in the past few years. The motivation behind this actively researched area is the fact that the limited spectrum available is currently being utilized in an inefficient way. The complete wireless spectrum is assigned and reserved, but not necessarily being used. At the same time, the demand for innovation in wireless technology is growing. Since there is no room in the wireless spectrum to allocate significant frequency bands for future wireless technologies, the only recourse is to increase utilization of the spectrum. To achieve this, we must find a way to share the spectrum. Spectrum sharing techniques will require coordination between all the layers of the protocol stack. The network and the wireless medium are inextricably linked and, thus, both must be considered when optimizing wireless network performance.
The current spectrum allocation policy allows the use of low cost standardized communication equipment and it can ensure that there will be no conflicts in the access to the licensed spectrum . However, it possesses some serious drawbacks that are of concern for future spectrum management.
1.10 Dynamic Spectrum Access
‘Dynamic spectrum access is a technique that allow unlicensed user(secondary user) to access the spectrum of licensed user(primary user) without knowing of primary user.’
1.11 Primary system
It refers to the licensed system with regulatory allocated spectrum. This system has the exclusive privilege to access the assigned spectrum range.
1.11.1 Secondary system
It refers to the unlicensed cognitive system(secondary user) and can only opportunistically access the spectrum hole which is currently not used by the primary system. Spectrum sharing strategy enables the secondary unlicensed system to dynamically access the licensed frequency bands in primary system without any modification to the devices, terminals, services, architectures and networks in the primary system.
In such case, the secondary system performs transparent for the primary system. Hence, on detecting the PU appearance in the particular frequency which is currently used by an SU, the SU has to vacate this frequency for the PU. At the same time, the SU will scan the frequency range and transfer its connection to another unused spectrum band if available. This refers to spectrum handoff procedure.
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Chapter 2
Literature Survey
Tragos, E.Z. et al, in “Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey” 2013 [1], the authors describe Cognitive radio (CR) has emerged as a promising technology to exploit the unused portions of spectrum in an opportunistic manner. The fixed spectrum allocation of governmental agencies results in unused portions of spectrum, which are called “spectrum holes” or “white spaces”. CR technology overcomes this issue, allowing devices to sense the spectrum for unused portions and use the most suitable ones, according to some pre-defined criteria. Spectrum assignment is a key mechanism that limits the interference between CR devices and licensed users, enabling a more efficient usage of the wireless spectrum. Interference is a key factor that limits the performance in wireless networks. The scope of this work is to give an overview of the problem of spectrum assignment in cognitive radio networks, presenting the state-of-the-art proposals that have appeared in the literature, analyzing the criteria for selecting the most suitable portion of the spectrum and showing the most common approaches and techniques used to solve the spectrum assignment problem. Finally, an analysis of the techniques and approaches is presented, discussing also the open issues for future research in this area.
Beibei Wang et al, in “Advances in cognitive radio networks: A survey” 2011 [2], the authors describe With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.
Yucek, T. et al, in “A survey of spectrum sensing algorithms for cognitive radio applications” 2009 [3], the authors describe The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.
Bkassiny, M. et al, in “A Survey on Machine-Learning Techniques in Cognitive Radios” 2013 [4], the authors describe In this survey paper, they characterize the learning problem in cognitive radios (CRs) and state the importance of artificial intelligence in achieving real cognitive communications systems. They review various learning problems that have been studied in the context of CRs classifying them under two main categories: Decision-making and feature classification. Decision-making is responsible for determining policies and decision rules for CRs while feature classification permits identifying and classifying different observation models. The learning algorithms encountered are categorized as either supervised or unsupervised algorithms. They describe in detail several challenging learning issues that arise in cognitive radio networks (CRNs), in particular in non-Markovian environments and decentralized networks, and present possible solution methods to address them. They discuss similarities and differences among the presented algorithms and identify the conditions under which each of the techniques may be applied.
Yuhua Xu et al, in “Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions” 2013 [5], the authors describe Opportunistic spectrum access (OSA) has been regarded as the most promising approach to solve the paradox between spectrum scarcity and waste. Intelligent decision making is key to OSA and differentiates it from previous wireless technologies. In this article, a survey of decision-theoretic solutions for channel selection and access strategies for OSA system is presented. They analyze the challenges facing OSA systems globally, which mainly include interactions among multiple users, dynamic spectrum opportunity, tradeoff between sequential sensing cost and expected reward, and tradeoff between exploitation and exploration in the absence of prior statistical information. They provide comprehensive review and comparison of each kind of existing decision-theoretic solution, i.e., game models, Markovian decision process, optimal stopping problem and multi-armed bandit problem. They analyze their strengths and limitations and outline further research for both technical contents and methodologies. In particular, these solutions are critically analyzed in terms of information, cost and convergence speed, which are key concerns for practical implementation. Moreover, it is noted that each kind of existing decision-theoretic solution mainly addresses one aspect of the challenges, which implies that two or more kinds of decision-theoretic solutions should be incorporated to address more challenges simultaneously.
Paisana, F. et al, in “Radar, TV and Cellular Bands: Which Spectrum Access Techniques for Which Bands?” 2014 [6], the authors describe Opportunistic access has been considered by regulators for a number of different spectrum bands. In this paper, they discuss and qualitatively evaluate techniques used in the discovery of spectrum opportunities, also called white spaces, in the radar, TV, and cellular bands. These techniques include spectrum sensing, cooperative spectrum sensing, geolocation databases, and the use of beacons. They make the case that each of the three bands considered calls for a different set of spectrum access techniques. While TV bands are well matched to the adoption of geolocation databases, a database-assisted spectrum sensing mechanism may represent the most efficient solution to exploit the spectrum holes in radar bands. They drew this conclusion based on a multitude of factors, such as the radar antennas’ constant motion, and the absence of a hidden node problems in these bands. The unpredictability of cellular systems, on the other hand, calls for a more coordinated spectrum access approach, namely beacon signaling, that could be implemented using the already established cellular infrastructure and spare bits of its logical channels.
Salami, G. et al, in “A Comparison Between the Centralized and Distributed Approaches for Spectrum Management” 2011 [7], the authors describe There is a growing demand for spectrum to accommodate future wireless services and applications. Given the rigidity of current allocations, several spectrum occupancy studies have indicated a low utilization over both space and time. Hence, to satisfy the demands of applications it can be inferred that dynamic spectrum usage is a required necessity. Centralized Dynamic Spectrum Allocation (DSA) and Distributed Dynamic Spectrum Selection (DSS) are two paradigms that aim to address this problem, whereby they use DSS (distributed) as an umbrella term for a range of terminologies for decentralized access, such as Opportunistic Spectrum Access and Dynamic Spectrum Access. This paper presents a survey on these methods, whereby they introduce, discuss, and classify several proposed architectures, techniques and solutions. Corresponding challenges from a technical point of view are also investigated, as are some of the remaining open issues. The final and perhaps most significant contribution of this work is to provide a baseline for systematically comparing the two approaches, revealing the pros and cons of DSA (centralized) and DSS (distributed) as methods of realizing spectrum sharing.
Gavrilovska, L. et al, in “Learning and Reasoning in Cognitive Radio Networks” 2013 [8], the authors describe Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.
Changwoo Lee et al, in “Exploiting Spectrum Usage Patterns for Efficient Spectrum Management in Cognitive Radio Networks” 2010 [9], the authors describe A cognitive radio (CR) is very significant technology to use a spectrum dynamically in wireless communication networks. However, very little has been done on using the spectrum usage patterns to handle with the problem of spectrum allocation in dynamic spectrum access. They suggest a scheme by exploiting spectrum usage patterns for the efficient spectrum management and reduce the communication cost in cognitive radio networks (CRNs). They propose the following three factors into account: spectrum sensing scheme with a sleep mode, spectrum decision scheme with a probability of spectrum access and spectrum handoff scheme with back-off time. All factors make use of spectrum usage patterns based on the statistical information. The first factor reduces the number of spectrum sensing. The second increases the opportunity of spectrum access and the last decreases the number of spectrum handoff. First of all, their proposed spectrum management scheme considers the analysis of the spectrum usage patterns and various factors obtained from the analysis is applied to lessen the communication cost in CRNs. The simulation results show that their proposed scheme improve the efficiency of spectrum management in dynamic spectrum access.
Won-Yeol Lee et al, in “Spectrum-Aware Mobility Management in Cognitive Radio Cellular Networks” 2012 [10], the authors describe Cognitive radio (CR) networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face several challenges based on the fluctuating nature of the available spectrum, making it more difficult to support seamless communications, especially in CR cellular networks. In this paper, a spectrum-aware mobility management scheme is proposed for CR cellular networks. First, a novel network architecture is introduced to mitigate heterogeneous spectrum availability. Based on this architecture, a unified mobility management framework is developed to support diverse mobility events in CR networks, which consists of spectrum mobility management, user mobility management, and intercell resource allocation. The spectrum mobility management scheme determines a target cell and spectrum band for CR users adaptively dependent on time-varying spectrum opportunities, leading to increase in cell capacity. In the user mobility management scheme, a mobile user selects a proper handoff mechanism so as to minimize a switching latency at the cell boundary by considering spatially heterogeneous spectrum availability. Intercell resource allocation helps to improve the performance of both mobility management schemes by efficiently sharing spectrum resources with multiple cells. Simulation results show that the proposed method can achieve better performance than conventional handoff schemes in terms of both cell capacity as well as mobility support in communications.
Jesuale, N. et al, in “A Policy Proposal to Enable Cognitive Radio for Public Safety and Industry in the Land Mobile Radio Bands” 2007 [11], the authors describe The frequency bands that have been licensed to the land mobile radio (LMR) services for decades are a tremendously fertile field for the deployment of cognitive radio technology. This paper outlines several reasons why policy-based cognitive radios would be particularly useful for modern public safety, federal non-military and business/industrial applications, especially in the VHF and UHF bands, where 80% of the public safety, federal and business/industrial licenses are currently held. This paper argues that many interoperability deficiencies are directly related to the original approach to spectrum policy and radio frequency regulation developed in the early 1920’s, which segmented uses of LMR spectrum into several use classes. It provides a historic perspective to explain why the current status of LMR infrastructure, operations and licensee behavior is a direct result of antiquated policies and technologies still applied and deployed in these bands. The paper discusses the reasons that cognitive radio could be a successful solution for the apparent congestion in the bands. It suggests that policy-based cognitive radio systems operated on a cooperative, shared basis could lower costs of use and aid coordination for emergency responders across both public and private sectors of the traditional LMR user community. They discuss policy reforms and innovations such as spectrum pooling and spectrum portability that could spur new shared infrastructure development and spectrum efficiencies. They suggest several key policy reforms for consideration, including immediate cessation of ongoing narrowbanding initiatives, decoupling of spectrum licenses from spectrum access, and national spectrum management by frequency coordinators.
Gronsund, P. et al, in “Towards spectrum micro-trading” 2012 [12], the authors describe Spectrum trading is an important tool to increase overall spectrum utilization and to open up opportunities for businesses to get access to desired spectrum. It has been implemented in some countries for some spectrum bands. At the same time, systems and architectures for cognitive radio technologies are being developed that are able to dynamically use spectrum bands in more flexible manners including functionalities such as dynamic bandwidth, spectrum band concatenation, sensing, channel switching and cognition. However, the current spectrum trading regimes usually require long times to execute a trade, hence limiting the flexibility over short time scales. In this paper they study spectrum micro-trading as a concept to enable trading of spectrum at the micro scale in at least three dimensions; on the micro-spatial, micro-temporal and micro-frequency scale. The ecosystem required for spectrum micro-trading and the most important metrics are defined; market viability, spectrum utilization, channel quality and social welfare. Furthermore, they propose a model for spectrum micro-trading, the Micro-trading Pixelation Model, which addresses the three dimensions on the micro scale. They then discuss the main challenges and issues that have to be solved for the realization of spectrum micro-trading with the proposed model. Finally, some initial simulation results are provided to demonstrate the performance of spectrum micro-trading.
Yong Ding et al, in “Video On-Demand Streaming in Cognitive Wireless Mesh Networks” 2013 [13], the authors describe Cognitive radio (CR), which enables dynamic access of underutilized licensed spectrums, is a promising technology for more efficient spectrum utilization. Since cognitive radio enables the access of larger amount of spectrum, it can be used to build wireless mesh networks with higher network capacity, and thus provide better quality of services for high bit-rate applications. In this paper, they study the multisource video on-demand application in multi-interface cognitive wireless mesh networks. Given a video request, they find a joint multipath routing and spectrum allocation for the session to minimize its total bandwidth cost in the network, and therefore maximize the number of sessions the network can support. They propose both distributed and centralized routing and channel allocation algorithms to solve the problem. Simulation results show that their algorithms increase the maximum number of concurrent sessions that can be supported in the network, and also improve each session’s performance with regard to spectrum mobility.
Guha, A. et al, in “Power allocation schemes for cognitive radios” 2008 [14], the authors describe Coexistence of one or more cognitive radios with a primary radio where the cognitive radios transmit in a spectrum allocated to the primary radio has been investigated in this paper. They consider two distinct scenarios: one in which the cognitive radios give higher priority to one of the cognitive radios and a second in which all the cognitive radios compete to get maximum possible data rates for themselves. In first scenario, the cognitive radio with higher priority gets a choice to decide how much of available resources it wants to use, and rest of the cognitive radios scavenge the left-over resources. They propose power allocation strategies to be used by the radios under both scenarios. They show that proposed power allocation strategy to be used in the first and cooperative scenario leads to overall system capacity improvement. They also prove convergence of power allocation algorithm proposed for the second and greedy scenario. In both cases the cognitive radios ensure that sum of interference caused by them in the spectrum remains below interference threshold.
Lili Cao et al, in “Distributed Rule-Regulated Spectrum Sharing” 2008 [15], the authors describe Dynamic spectrum access is a promising technique to use spectrum efficiently. Without being restricted to any prefixed spectrum bands, nodes choose operating spectrum on-demand. Such flexibility, however, makes efficient and fair spectrum access in large-scale networks a great challenge. Prior work in this area focused on explicit coordination where nodes communicate with peers to modify local spectrum allocation, and may heavily stress the communication resource. In this paper, they introduce a distributed spectrum management architecture where nodes share spectrum resource fairly by making independent actions following spectrum rules. They present five spectrum rules to regulate node behavior and maximize system fairness and spectrum utilization, and analyze the associated complexity and overhead. They show analytically and experimentally that the proposed rule-based approach achieves similar performance with the explicit coordination approach, while significantly reducing communication cost.
Mangold, S. et al, in “Operator Assisted Cognitive Radio and Dynamic Spectrum Assignment with Dual Beacons – Detailed Evaluation” 2006 [16], the authors describe Dynamic spectrum assignment refers to a new approach for radio regulation. Whereas today either command-and-control licensing, or alternatively unlicensed bands are used to coordinate the radio spectrum utilization, it is envisioned to coordinate the spectrum utilization in a more flexible way with the help of dynamic spectrum assignment. With this new approach, radio systems will share spectrum either horizontally with distributed spectrum allocation such as listen-before-talk and equal rights to access the radio spectrum, or vertically, where so-called primary radio systems have higher priority to access the radio spectrum than the so-called secondary radio systems. Horizontal and vertical spectrum sharing are often discussed in the context of opportunistic spectrum usage and cognitive (secondary) radio. To guarantee the spectrum access priorities when spectrum is shared, and to coordinate the vertical spectrum sharing between primary and cognitive radio systems, beaconing concepts are often proposed but criticized for their poor reliability in hidden station scenarios. In this paper, they discuss an improved beaconing concept that is based on operator assistance with two instead of one single beacon. Our analysis illustrates that their proposed dual beacon concept provides high flexibility and at the same time improves the reliability of spectrum coordination
Wang Fan et al, in “Researching on future spectrum management based on cognitive radio” 2010 [17], the authors describe The influence of cognitive radio to future spectrum management was discussed through the aspects of management. And this paper has discussed the goals and purpose of the future spectrum management and announced that the future spectrum management function will be placed on the cognitive radio nodes. They give the point that the independence between policies and behaviors, behaviors and protocols can improve the mobility of spectrum management and brings the flexibility. And architecture of the future spectrum management involved a spectrum database and a policy database has been proposed. Also, a proposed layering framework of future spectrum management was presented, and the relative management processing is demonstrated too.
Chung-Wei Wang et al, in “Analysis of Reactive Spectrum Handoff in Cognitive Radio Networks” 2012 [18], the authors describe In this paper, they present an analytical framework to evaluate the effects of multiple spectrum handoffs on channel utilization and latency performances in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs. In order to decide the target channel for each spectrum handoff and resume the unfinished transmission, wideband sensing is performed in an on-demand reactive manner. Although spectrum handoff procedure can enhance channel utilization, transmission latency of the secondary users is prolonged due to multiple handoffs. Thus, two fundamental issues in CR networks with multiple spectrum handoffs arise: (1) to what extent the channel utilization can be improved; and (2) how long the transmission latency will be extended for the secondary users. To solve the first problem, they introduce the preemptive resume priority (PRP) M/G/1 queueing network to characterize the channel usage behaviors of CR networks. Based on this queueing network, channel utilization under various traffic arrival rates and service time distributions can be evaluated. Furthermore, on top of the proposed queueing network, a state diagram is developed to characterize the effects of multiple handoff delay on the transmission latency of the secondary users. The analytical results can provide a helpful insight to study the effects of traffic arrival rates and service time on channel utilization and transmission latency and then facilitate the designs of admission control rules for the secondary users subject to their performance requirements.
Axell, E. et al, in “Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances” 2012 [19], the authors describe The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).
Abadie, A. et al, in “Leveraging an Inventory of the Cognitive Radio Attack Surface” 2012 [20], the authors describe The cognitive radio is an emerging technology that holds great promise due to its adaptive nature and ability to exploit the advantages inherent to software defined radios (SDR). The cognitive radio’s potential to address spectrum access challenges resulting from an exponential increase in the number of network devices will make it a leading technology of the next decade. For this reason, it is imperative to investigate the security considerations of the cognitive radio in its infancy rather than attempt to address them at maturity. Though research in the vulnerabilities of cognitive radio exists, few explore the threat vectors against both the cognitive engine and the underlying SDR infrastructure within which it operates. This paper takes such a holistic approach, and then further contributes to the research area by offering three misuse cases that portray how the vulnerabilities discussed could be exploited. These cases visualize the risk environment based on a denial of service, an advanced persistent threat, and an insider threat and will allow for security practitioners to anticipate how their organizations may be impacted. Moreover, by introducing a simulation of the threat one can use existing principles to analyze its prosecution (i.e. attack graphs, evidence graphs, etc.).
Brown, T.X. et al, in “Policy-based radios for UAS operations” 2012 [21], the authors describe Unmanned aircraft systems need sufficient radio spectrum for remote command and control, payload operation, and flight safety. Spectrum is a scarce resource and unmanned aircraft operators cannot depend on new spectrum allocations. Though all spectrum is already allocated, paradoxically, most spectrum goes unused at any given time. This unused spectrum is a complex patchwork over time and space that cannot be accessed with ordinary radios. A policy-based radio is a type of cognitive radio technology that is guided by so-called policies of what spectrum is available when and where and under what conditions. This paper describes the enabling role that policy-based radios can have for unmanned aircraft systems to have safe, efficient, and reliable access to the spectrum. It describe how they can be adapted to meet the unique spectrum challenges of unmanned aircraft and provides analysis and examples to show that they may be able to provide additional spectrum.
Chandra, A. in “Spectrum management for future generation wireless based technology” 2009 [22], the authors describe Radio Spectrum is an essential raw material for most promising industries of the future. Many new emerging technologies as well as existing services require spectrum for their growth. This has put a great pressure on radio frequency spectrum regulator for its efficient use. It is commonly believed that there is a crisis of spectrum availability but studies have shown that not all the spectrum is in use for all of the time i.e. there is no shortage of radio spectrum only lack of efficient management. The traditional command and control method of spectrum management does not lead to efficient spectrum management. There are several challenges before spectrum regulator to meet the future demands of spectrum like interference Management, international coordination, Technology Neutrality etc. To overcome these challenges, instead of rigid, a flexible spectrum management is essentially required. The objective of new spectrum management should be designed in such a way that it should meet the above challenges as well as it should be economically, technically efficient and at the same time protect the public interest also.
Ohyun Jo et al, in “Efficient spectrum matching based on spectrum characteristics in cognitive radio systems” 2008 [23], the authors describe QoS is a very important issue in cognitive radio systems. They present and discuss spectrum matching algorithms to support QoS for cognitive radio users. This work is carried out in the context of a dynamic changing spectrum. In this paper, they propose efficient spectrum matching algorithms, which have low-complexity and quite good performance, based on statistical characteristics of spectrum bands. Those are classified random matching algorithm and greedy algorithm. The classified random matching algorithms would match the most suitable spectrum class to a proper user and randomly choose a spectrum within the class. And, using greedy algorithm, the best spectrum out of available spectrum bands will be chosen whenever a new user joins. The proposed algorithms provide QoS for cognitive radio systems through the reduction of spectrum handoff probability and improvement of fairness. Performance analysis and simulation results validate the efficiency of the proposed algorithms.
Bicen, A. et al, in “Dedicated Radio Utilization for Spectrum Handoff and Efficiency in Cognitive Radio Networks” 2013 [24], the authors describe To perform spectrum handoff, cognitive radio (CR) nodes communicating with each other need to exchange licensed user detection information, i.e., perform spectrum coordination, over a common control channel. The spectrum coordination can be fulfilled either via existing cognitive radio interface with time division or via a separate dedicated radio, i.e., a common control interface (CCI), continuously. CR nodes with CCI can instantly exchange licensed user detection information and cease frame transmission, while spectrum coordination can only be performed after the frame transmission period without CCI. Nevertheless, the impact of CCI incorporation into CR nodes in terms of common performance metrics must be thoroughly assessed to evaluate the worthiness of additional radio cost. In this paper, an analytical framework is presented to assess the impact of CCI incorporation into CR nodes for spectrum handoff. The developed framework enables analyzing potential benefits and disadvantages of employing CCI for spectrum handoff, in terms of achievable delay, energy consumption, spectrum utilization and event estimation performance. Extensive performance evaluations are presented to illustrate the impact of CCI utilization on efficiency of spectrum handoff. The network and communication regimes that would yield having CCI favorable are characterized in terms of spectrum conditions and CR parameters.
Wysocki, T. et al, in “Pricing of Cognitive Radio Rights to Maintain the Risk-Reward of Primary User Spectrum Investment” 2010 [25], the authors describe Cognitive Radio (CR) has been recently proposed as a method of alleviating the shortage of radio spectrum, by increasing the efficiency of spectrum use. However, as large portions of spectrum remain under long-term licenses, the economic welfare of the primary license holders must be taken into account, when considering methods of spectrum access that may degrade license holder Quality-of-Service (QoS) and therefore revenue. Several price discovery methods have been proposed to find the fee that license holders should charge for cognitive access to their spectrum. This paper examines the spectrum licenses themselves as an investment class. By performing a reward-to-variability (Sharpe Ratio) analysis of the spectrum license under different levels of CR activity, a floor price for CR access is derived such that the quality of the license holder’s spectrum investment from a Sharpe Ratio point of view is not degraded. An example scenario is provided to illustrate this pricing mechanism, and simulation results illustrate its effectiveness in maintaining the quality of the license holder’s spectrum investment.
Li-Chun Wang et al, in “Load-Balancing Spectrum Decision for Cognitive Radio Networks” 2011 [26], the authors describe In this paper, they present an analytical framework to design system parameters for load-balancing multiuser spectrum decision schemes in cognitive radio (CR) networks. Unlike the non-load-balancing methods that multiple secondary users may contend for the same channel, the considered load-balancing schemes can distribute the traffic loads of secondary users to multiple channels. Based on the preemptive resume priority (PRP) M/G/1 queueing theory, a spectrum decision analytical model is proposed to evaluate the effects of multiple interruptions from the primary user during each link connection, the sensing errors (i.e., missed detection and false alarm) of the secondary users, and the heterogeneous channel capacity. With the objective of minimizing the overall system time of the secondary users, they derive the optimal number of candidate channels and the optimal channel selection probability for the sensing-based and the probability-based spectrum decision schemes, respectively. They find that the probability-based scheme can yield a shorter overall system time compared to the sensing-based scheme when the traffic loads of the secondary users is light, whereas the sensing-based scheme performs better in the condition of heavy traffic loads. If the secondary users can intelligently adopt the best spectrum decision scheme according to sensing time and traffic conditions, the overall system time can be improved by 50% compared to the existing methods.
Hong-jie Liu et al, in “Study on the performance of spectrum mobility in cognitive wireless network” 2008 [27], the authors describe Current frequency allocation scheme results in poor spectrum utility. In some frequency bands, the spectrum may not be fully utilized either on geographical or at temporal level. In addition, on some hot spot business frequency bands, spectrum resources become in short supply. Many papers discuss the new spectrum management policies, and the focus is dynamic spectrum access (DSA) technology based on cognitive radio. However, spectrum mobility and spectrum handoff are new challenges for cognitive wireless networks. Little research addresses these fields now. In this paper, they discuss concepts of spectrum mobility/handoff and research probability models of spectrum holes and cognitive user??s behavior. A time relationship model of spectrum handoff is proposed. They also research the impact of spectrum mobility and service duration of cognitive user on spectrum handoff probability. Finally, theoretical and simulation results of spectrum handoff probability are obtained.
Sheng-Yuan Tu et al, in “Spectrum Sensing of OFDMA Systems for Cognitive Radio Networks” 2009 [28], the authors describe To fully exploit wireless radio resource and, thus, increase spectrum efficiency, cognitive radios shall sense wireless environments and identify interference to allow opportunistic transmissions for secondary systems. Based on Chen in their work about a terminal architecture for cognitive radio networks, by further obtaining transmission information with a rate-distance nature that is extended from an overlay concept, secondary systems can even leverage busy duration of primary systems to enhance the opportunity to use spectrum under derived tolerable interference to the primary orthogonal frequency-division multiple access (OFDMA) system. Our proposed novel multistate (i.e., existence, activity, and data transmission rate) spectrum sensing can thus effectively acquire a set of cognitive information for OFDMA mobile communication systems under a general system model. Through the received signal strength indicator (RSSI), fundamental symbol rate, cyclic property of the OFDMA signal, and the control and management signal, they can precisely determine the existence and activity of the primary (OFDMA) system within a subband via Neyman-Pearson (NP) criterion. With a priori knowledge of the frame structure of potential primary systems, communication parameters, including data transmission rate and resource allocation state, can be extracted by analyzing the frame header. Through appropriate parametric adjustments, their sensing procedure can be extended to state-of-the-art OFDMA systems.
Peng Jia et al, in “Capacity- and Bayesian-Based Cognitive Sensing with Location Side Information” 2011 [29], the authors describe They investigate spectrum sensing by energy detection based on two different objective functions: a Bayesian sensing cost or the network weighted sum capacity. The Bayesian cost is a traditional detection measure which aims at minimizing a combination of the miss-detection and false-alarm probabilities, while the capacity objective is a communication measure which aims at maximizing the network throughput. Fading-dependent optimal sensing thresholds for each objective are derived in closed-form for different cases of location side information. To make sensing more robust to channel fading, they also propose fading-independent sub-optimal thresholds. Results show that location side information helps improve performance when using the threshold designed for that performance measure. However, the Bayesian-based threshold does not utilize the side information well in improving the network sum capacity. On the other hand, the capacity-based threshold captures the benefit of side information in both the capacity and Bayesian cost measures. Furthermore, it helps to significantly improve the network throughput. The proposed sensing schemes with location side information can also be generalized to a network with multiple cognitive users in a simple and distributed manner.
Jiang, T. et al, in “Efficient exploration in reinforcement learning-based cognitive radio spectrum sharing” 2011 [30], the authors describe This study introduces two novel approaches, pre-partitioning and weight-driven exploration, to enable an efficient learning process in the context of cognitive radio. Learning efficiency is crucial when applying reinforcement learning to cognitive radio since cognitive radio users will cause a higher level of disturbance in the exploration phase. Careful control of the tradeoff between exploration and exploitation for a learning-enabled cognitive radio in order to efficiently learn from the interactions with a dynamic radio environment is investigated. In the pre-partitioning scheme, the potential action space of cognitive radios is reduced by initially randomly partitioning the spectrum in each cognitive radio. Cognitive radios are therefore able to finish their exploration stage faster than more basic reinforcement learning-based schemes. In the weight-driven exploration scheme, exploitation is merged into exploration by taking into account the knowledge gained in exploration to influence action selection, thereby achieving a more efficient exploration phase. The learning efficiency in a cognitive radio scenario is defined and the learning efficiency of the proposed schemes is investigated. The simulation results show that the exploration of cognitive radio is more efficient by using pre-partitioning and weight-driven exploration and the system performance is improved accordingly.
Zhi Wang et al, in “Main challenges in practical applications of cognitive radio” 2011 [31], the authors describe Facing more and more serious scarcity of radio resources in today’s society, cognitive radio offers a great solution from the theoretical level. But in the process of converting theoretical research into practical application, cognitive radio still has lots of inevitable problems. In this paper, demonstration about problems the cognitive radio encounters in practical applications is made from three aspects of spectrum sensing, spectrum handoff and information security. The analysis of the reasons why the problems occur and summary of present solutions are also introduced in the article.
Zhu Ji et al, in “Multi-Stage Pricing Game for Collusion-Resistant Dynamic Spectrum Allocation” 2008 [32], the authors describe In order to fully utilize scarce spectrum resources, dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. However, the collusion among selfish network users may seriously deteriorate the efficiency of dynamic spectrum sharing. The network users’ behaviors and dynamics need to be taken into consideration for efficient and robust spectrum allocation. In this paper, they model the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. In order to combat user collusion, they propose a pricing-based collusion-resistant dynamic spectrum allocation approach to optimize overall spectrum efficiency, while not only keeping the participating incentives of the selfish users but also combating possible user collusion. The simulation results show that the proposed scheme achieves high efficiency of spectrum usage even with the presence of severe user collusion.
Zahed, S. et al, in “Performance Evaluation of Cognitive Radio Networks under Licensed and Unlicensed Spectrum Bands” 2014 [33], the authors describe One of the major challenges of Cognitive Radio (CRNs) is the spectrum handoff issue. Spectrum handoff happens when a Primary Users (PUs) appears in a spectrum band that is occupied by a Secondary User (SU). In such a case, SU should empty this spectrum band and perform a handoff procedure and search for an available free one. This process will be continued until the SU completes its data transmission. To avoid multiple spectrum handoffs, the spectrum handoff procedure should be performed in the unlicensed channels rather than the licensed channels. Thus, the number of handoffs can be reduced as no more spectrum handoffs will occur since all users have priority in this type of spectrum channel. This technique will help secondary users’ QoS from degradation. This paper proposes a prioritized spectrum handoff decision scheme in a mixture spectrum environment of unlicensed and licensed channels, in order to reduce the handoff delay. The licensed channels in the proposed scheme have been modelled using a pre-emptive resume priority (PRP) M/M/C queue. In contrast, the unlicensed channels have been modelled using an M/M/C retrial priority queue. In order to examine the performance of the implemented model, the handoff and new SUs are considered with equal and different priorities. Experimental results show that the prioritized handoff scheme outperforms the other scheme in terms of average handoff delay under various traffic arrival rates as well as the number of licensed and unlicensed channels used.
NoroozOliaee, M. et al, in “Analyzing Cognitive Network Access Efficiency Under Limited Spectrum Handoff Agility” 2014 [34], the authors describe Most existing studies on cognitive-radio networks assume that cognitive users (CUs) can switch to any available channel, regardless of the frequency gap between a target channel and the current channel. However, due to hardware limitations, CUs can actually jump only so far from where the operating frequency of their current channel is. This paper studies the performance of cognitive-radio networks while considering realistic channel handoff agility, where CUs can only switch to their neighboring channels. They use a continuous-time Markov process to derive and analyze the forced termination and blocking probabilities of CUs. Using these derived probabilities, they then study and analyze the impact of limited spectrum handoff agility on cognitive spectrum access efficiency. They show that accounting for realistic spectrum handoff agility reduces performance of cognitive-radio networks in terms of spectrum access capability and efficiency.
Jahed, T. et al, in “Design challenges and scope for cognitive radio wireless networks in Bangladesh” 2011 [35], the authors describe Standards groups and regulatory bodies around the world are increasingly seeking new ways of using and allowing access to allocating spectrum. In most parts of the world, cellular network bands are overloaded, but amateur radio and paging frequencies are not. Independent studies performed in some countries confirmed that observation, and concluded that spectrum utilization depends strongly on time and place. Moreover, fixed spectrum allocation prevents rarely used frequencies (those assigned to specific services) from being used by unlicensed users, even when their transmissions would not interfere at all with the assigned service. In this research they studied the frequency usage pattern of a large telecom company to identify the possibility of establishing a cognitive radio network using their licensed band. Experimental simulation results shows that a carefully designed network will boost the throughput significantly.
Lingjie Duan et al, in “Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator’s Perspective” 2011 [36], the authors describe This paper studies the optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who often leases spectrum via long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by both sensing the empty of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match demands of the secondary unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is typically cheaper, but the obtained useful spectrum amount is random due to primary licensed users’ stochastic traffic. The C-MVNO needs to determine the optimal amounts of spectrum sensing and leasing by evaluating the trade-off between cost and uncertainty. The C-MVNO also needs to determine the optimal price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. They model and analyze the interactions between the C-MVNO and secondary unlicensed users as a Stackelberg game. They show several interesting properties of the network equilibrium, including threshold structures of the optimal investment and pricing decisions, the independence of the optimal price on users’ wireless characteristics, and guaranteed fair and predictable QoS among users. They prove that these properties hold for general SNR regime and general continuous distributions of sensing uncertainty. They show that spectrum sensing can significantly improve the C-MVNO’s expected profit and users’ payoffs.
Chengbiao Li et al, in “Aggregation based spectrum allocation in cognitive radio networks” 2013 [37], the authors describe In cognitive radio networks, the available vacant frequency bands, referred to as spectrum holes, are usually discontinuous. Hence, it is hard to exploit these vacant frequency bands since the bandwidth of single one may not be able to meet the wide bandwidth requirement of secondary users (SUs). Spectrum aggregation (SA) enables SUs to access several discontinuous spectrum holes simultaneously to satisfy the wide bandwidth requirement of SUs, which greatly improves the utilization of spectrum resources. In this paper, they firstly propose an aggregation based spectrum allocation scheme based on Knapsack Problems by considering both hardware constraints and different bandwidth requirement of SUs. Then, a novel spectrum allocation algorithm called Aggregation Spectrum Allocation Algorithm (ASAA) is given. ASAA is evaluated in terms of the total bandwidth SUs can access and the spectrum utilization efficiency. Numerical results show that the proposed algorithm can achieve better performances compared to the existing algorithms.
Wang, Li-Chun et al, in “Modeling and Analysis for Spectrum Handoffs in Cognitive Radio Networks” 2012 [38], the authors describe In this paper, they present an analytical framework to evaluate the latency performance of connection-based spectrum handoffs in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs and the need of predetermining a set of target channels for spectrum handoffs. To quantify the effects of channel obsolete issue on the target channel predetermination, they should consider the three key design features: 1) general service time distribution of the primary and secondary connections; 2) different operating channels in multiple handoffs; and 3) queuing delay due to channel contention from multiple secondary connections. To this end, they propose the preemptive resume priority (PRP) M/G/1 queuing network model to characterize the spectrum usage behaviors with all the three design features. This model aims to analyze the extended data delivery time of the secondary connections with proactively designed target channel sequences under various traffic arrival rates and service time distributions. These analytical results are applied to evaluate the latency performance of the connection-based spectrum handoff based on the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard. Then, to reduce the extended data delivery time, a traffic-adaptive spectrum handoff is proposed, which changes the target channel sequence of spectrum handoffs based on traffic conditions. Compared to the existing target channel selection methods, this traffic-adaptive target channel selection approach can reduce the extended data transmission time by 35 percent, especially for the heavy traffic loads of the primary users.
Suga, J. et al, in “Optimal policy for joint spectrum resource management in multi-band cognitive radio networks” 2012 [39], the authors describe Cognitive Radio (CR) has been recognized as a promising solution to solve the current spectrum inefficiency problem. They consider a CR Base Station (CR-BS) has multiple spectrum bands over a wide frequency range and uses channel-dependent data rates to communicate with the Secondary Users (SUs). To maximize the spectrum utilization while supporting the Quality of Service (QoS) of the SUs, various decisions needs to be made during the operational period of the CR system taking into account not only the behavior of Primary Users (PUs) and SUs but also the current utilization of each spectrum band. In this paper, they propose an optimal policy for joint spectrum resource management in multi-band CR networks, which consists of admission control, spectrum band selection, spectrum handover and eviction control. The problem is formulated as a Semi-Markov Decision Process (SMDP) and the optimal decision for each system state is derived to reduce the SU blocking probability while keeping the SU dropping probability upper-bounded. Performance evaluation shows that the proposed policy outperforms other three policies in terms of both the SU blocking probability and the SU dropping probability.
Lin Gao et al, in “Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach” 2011 [40], the authors describe Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, they consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. They model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. They design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. They propose the necessary and sufficient conditions for the contract to be feasible. They further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, they analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and they find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.
Qingkai Liang et al, in “A Distributed-Centralized Scheme for Short- and Long-Term Spectrum Sharing with a Random Leader in Cognitive Radio Networks” 2012 [41], the authors describe In Cognitive Radio Networks (CRNs), Primary Users (PUs) can share their idle spectra with Secondary Users (SUs) under certain mechanisms. In this paper, they propose a distributed-centralized and incentive-aware spectrum sharing scheme for the multiple-PU scenario, which introduces a Random Leader who is elected randomly from SUs or PUs. The distributed aspect of their scheme lies in that it requires no central control entities, which can be independently implemented within a distributed spectrum market. The centralized aspect is that the leader draws up and assigns the socially optimal contracts for all PUs and SUs in a centralized manner, which maximizes the throughput of the whole network and attains the economic robustness (including Incentive Compatibility and Individual Rationality). Analysis shows that the proposed scheme takes in the advantages of both centralized and distributed schemes but overcomes their weaknesses. They use the proposed scheme to study two sharing scenarios: the short-term and the long-term spectrum sharing. The Short-Term Sharing (STS) focuses on distributing PUs’ idle spectra within one time slot while the Long-Term Sharing (LTS) considers multiple slots, where the spectrum mobility must be investigated. As an integrated design of both STS and LTS, their scheme not only fulfils SUs’ heterogeneous spectrum requirements but also obtains the socially optimal throughput while accounting for both SUs’ and PUs’ incentives.
Zhongshan Zhang et al, in “Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey” 2013 [42], the authors describe Cognitive radio is regarded as a promising technology to provide high bandwidth to mobile users via heterogeneous wireless network architectures and dynamic spectrum access techniques. However, cognitive radio networks may also impose some challenges due to various factors such as the ever increasing complexity of network architecture, the high cost of configuring and managing large-scale networks, the fluctuating nature of the available spectrum, diverse QoS requirements of various applications, and the intensifying difficulties of centralized control. A plethora of work has been carried out to address the challenges aforementioned by employing cognitive radio functionalities with self-organization features. In this article, variant aspects of self-organization paradigms in cognitive radio networks, including critical functionalities of MAC- and network-layer operations, are surveyed. The main contributions of this survey include introducing the fundamentals of existing cognitive radio and self-organization techniques as well as their current progress, surveying critical cognitive radio issues (including common control channel management, cooperative spectrum sensing, bioinspired spectrum sharing, network scalability and adaptive routing) as well as their self-organization features, and identifying new directions and open problems in cognitive radio networks.
Tabassam, A.A. et al, in “Building cognitive radios in MATLAB Simulink A step towards future wireless technology” 2011 [43], the authors describe Cognitive Radio (CR) is a future radio technology that is aware of its environment, internal state and can change its operating behavior (transmitter parameters) accordingly. It is intended to coexist with primary users (PUs) for using the underutilized spectrum without any harmful interference. Its key domains are sensing, cognition and adaptation. This paper presents a detailed survey of coexistence techniques used in IEEE 802 wireless network standards family to reduce the interference between different types of networks. It also presents a prototype system for designing and testing cognitive radios built on top of software defined radio in a MATLAB/-Simulink and interfaced with a universal software radio peripheral-2 (USRP2) main-board and RFX2400 daughter board from Ettus Research LLC. The philosophy behind the prototype is to sense, predict and adjust the operating parameters to achieve the desired objectives. The PUs detection (sensing) is performed using the statistical (non-parametric) spectrum estimation techniques, which are more useful in a noisy environment. Regression-based statistical time-series modeling & analysis are carried for PU’s channel behavior prediction for spectrum decisions. Software-defined radio is used for reconfigurability to adjust the operating parameters of the cognitive radios.
Sethi, A. et al, in “Hammer Model Threat Assessment of Cognitive Radio Denial of Service Attacks” 2008 [44], the authors describe With the advent of cognitive radios, existing wireless networks are expected to undergo a radical change in how they operate. Traditional wireless devices operate in fixed frequency bands and follow fixed network protocols set at the time of manufacture, unlike the emerging wireless devices based on cognitive radio technology which are expected to operate across multiple frequency bands with a variety of protocols that can change over the life of the device. While the wireless networks in which a cognitive radio device operates may implement device authentication, integrity checks and other higher-layer security mechanisms; the possibility of physical layer attacks, such as jamming attacks, still exists. This research work focuses on assessing potential cognitive radio specific physical layer attacks using the so-called Hammer model. In particular, they investigate vulnerabilities that may prevent CR communication in specific bands, completely deny a cognitive radio to communicate or induce it to cause harmful interference to existing users; so called denial-of-service attacks. In the process, they identify, analyze, and assess the risk level posed by the potential attacks in the different CR design paradigms proposed by different research groups. Further, this work recommends the most and least susceptible of the CR design paradigms under consideration.
Wei Feng et al, in “Joint Optimization of Spectrum Handoff Scheduling and Routing in Multi-hop Multi-radio Cognitive Networks” 2009 [45], the authors describe Spectrum handoff causes performance degradation of the cognitive network when the primary user reclaims its right to access the licensed spectrum. In a multi-hop cognitive network, this problem becomes even worse since multiple links are involved. Spectrum handoff of multiple links seriously affects the network connectivity and routing. In this paper, they describe a cross-layer optimization approach to solve the spectrum handoff problem with joint consideration of spectrum handoff scheduling and routing. They propose a protocol, called Joint Spectrum Handoff Scheduling and Routing Protocol (JSHRP). This paper makes the following major contributions. First, the concept “spectrum handoff of single link” is extended to “spectrum handoff of multiple links”, termed as “multi-link spectrum handoff”. Second, they define the problem of coordinating the spectrum handoff of multiple links to minimize the total spectrum handoff latency under the constraint of the network connectivity. This problem is proven to be NP-hard, and they propose both centralized and distributed greedy algorithms to minimize the total latency of spectrum handoff for multiple links in a multi-hop cognitive network. Moreover, they jointly design the rerouting mechanism with spectrum handoff scheduling algorithm to improve the network throughput. Different from previous works in which rerouting is performed after spectrum handoff, their rerouting mechanism is executed before the spectrum handoff really happens. Simulation results show that JSHRP improves the network performance by 50% and the higher degree of interference the cognitive network experiences, the more improvement their solution will bring to the network.
Rong Yu et al, in “Hybrid Spectrum Access in Cognitive-Radio-Based Smart-Grid Communications Systems” 2014 [46], the authors describe Cognitive-radio-based smart-grid networks have been studied recently as an efficient and reliable communications infrastructure for the future power grid. In this paper, they consider the spectrum resource management in cognitive-radio-based smart-grid networks. A new spectrum access paradigm called hybrid spectrum access (HSA) is proposed, in which both licensed and unlicensed spectrum bands are intelligently scheduled for the transmission of smart-grid services. The admission control problem under HSA is deliberately investigated. Furthermore, the impact of spectrum sensing error on the performance of HSA is analyzed by using a multidimensional Markov chain. Regarding the practical applications of the smart grid, two optimization problems, namely, cost-driven spectrum leasing and quality of service (QoS)-driven spectrum management, are formulated. Numeric results indicate that the HSA strategy is able to significantly improve the QoS of the smart-grid services, save the cost in spectrum leasing, and maintain the system interference at a sufficiently low range.
Sonnenberg, J. et al, in “Quantifying the relative merits of genetic and swarm algorithms for network optimization in cognitive radio networks” 2012 [47], the authors describe Cognitive engines have been under study and development for a number of years as a technique for addressing the needs of cognitive radios [1,2,3]. More recently there has been effort to expand the role of the cognitive engine to address the needs of a network of cognitive radios [4,5]. Haykin [6] has demonstrated that there is a significant difference between a network of cognitive radios and a cognitive radio network. This paper addresses three questions: 1. What are the significant functional and parametric differences between cognitive algorithms that deal with optimizing the operations of a cognitive radio and cognitive algorithms that optimize the operations of a cognitive radio network? 2. What are the trade-offs in applying the various algorithms to each task? 3. Which algorithms are optimal for the networking tasks? This paper identifies a set of parameters that characterize candidate algorithms and explores the benefits and drawbacks of each for cognitive network tasks. They propose a tiered architecture of cognitive engine algorithms that work in tandem to optimize the use of cognitive networked radios for the optimal success of the networked mission.
Li-Chun Wang et al, in “Spectrum management techniques with QoS provisioning in cognitive radio networks” 2010 [48], the authors describe In this paper, they discuss the spectrum management technologies including spectrum sensing, spectrum decision, spectrum sharing, and spectrum handoff schemes in cognitive radio (CR) networks. In order to evaluate performance of different spectrum management schemes, the preemptive resume priority (PRP) M/G/1 queuing model is suggested to characterize the spectrum usage behavior between the primary and the secondary users. Based on this model, they derive a new performance measure the overall system time of the secondary connections. The overall system time is an important performance measure to provide the quality of service (QoS) provisioning service for the secondary users. It is defined as the duration from the instant that data arrives at system until the instant of finishing the whole transmission. Clearly, multiple interruptions from the high-priority primary users will increase the overall system time of the low-priority secondary users. Based on the PRP M/G/1 queuing model, the impacts of multiple interruptions on the overall system time can be evaluated. On top of this model, a spectrum sensing, a spectrum decision, a spectrum sharing, and a spectrum handoff algorithms are investigated to reduce the overall system time. From the numerical results, they can design better spectrum management policies to satisfy the QoS requirement of the secondary users in CR networks and provide useful insight into the design tradeoff for different spectrum management technologies.1
Brown, T.X. in “Policy-based radios for spectrum sharing” 2013 [49], the authors describe This paper describes a new radio technology, policy-based radios, which has the potential to enable greater access to the radio spectrum while enhancing and simplifying spectrum management. Spectrum management faces both challenges and opportunities in developing countries. The role for policy-base radios in this environment is described as well as key design decisions for their use.
Xi Su et al, in “A New Mechanism of Dynamic Spectrum Allocation in the Cognitive Network” 2009 [50], the authors describe Cognitive radio is thought to be a flexible way to improve the spectrum efficiency. The major challenge is that the communications in the cognitive radio network would be affected by the behaviors of licensed incumbents. The cognitive system must vacate the channel within moving time whenever the licensed incumbents re-occupy that channel, in order to reduce the interference to the licensed system. Besides, in the licensed system, the channels are used by licensed incumbents independently, therefore, the licensed channels have different occupancy ratio. In this study, they propose an improved mechanism considering the channels characteristics in spectrum allocation. The new method can make use of the spectrum characteristics to maximum the spectrum availability and reduce the probability of drop rate in the cognitive system. Besides, their proposed scheme can guarantee the fairness among multi-cells. Through simulation results, it is shown that the proposed scheme can enhance performance of the cognitive network.
Rashid, R.A. et al, in “Enabling dynamic spectrum access for cognitive radio using software defined radio platform” 2011 [51], the authors describe This Spectrum occupancy measurement in many countries shows sporadic and poorly utilized licensed radio spectrum due to the outdated exclusive spectrum access policy. Dynamic Spectrum Access (DSA) technique allows cognitive (unlicensed) user (CU) to opportunistically transmit in a primary (licensed) user (PU) frequency band for a given time if it does not detect any ongoing operations. In this work, DSA is realized using cognitive radio (CR) technology due to its features of able to sense, learn, adapt and react according to the environment. The proposed design of the DSA based CR system consists of four main functional blocks: spectrum sensing, spectrum management, spectrum decision and data transmission. The implementation is done using GNU Radio and Universal Software Radio Peripheral (USRP) Software Defined Radio (SDR) platform. The result shows a significant improvement in term of Packet Reception Rate (PRR) in DSA based CR systems compared to the system without DSA capability.
Berlemann, L. et al, in “Spectrum load smoothing for cognitive medium access in open spectrum” 2005 [52], the authors describe Today’s framework for radio spectrum regulation and the way the usage of radio spectrum is coordinated, is undergoing vital changes. In the face of scarce radio resources, regulators, industry, and the research community are initiating promising approaches towards a more flexible spectrum usage, referred to as open spectrum. In this paper they discuss medium access control protocols for spectrum agile radios that opportunistically use spectrum, also referred to as “cognitive radio”. Spectrum agile radios operate in parts of the spectrum originally licensed to other radio services. They identify free spectrum, coordinate its usage and release it when this is required by licensed radio systems. The application of “waterfilling” from the information theory, referred to as spectrum load smoothing (SLS), and its realization in IEEE 802.11e-based spectrum agile wireless networks is examined in this paper. The SLS, as intelligent principle of spectrum usage, targets at the distributed quality-of-service support in scenarios of coexisting spectrum agile radios. With SLS, spectrum agile radios observe the past usage of the spectrum, while at the same time a harmful interference to license holding radio systems is avoided. The SLS can therefore be referred to as cognitive medium access. In this paper, the capability to support quality-of-service in the presence of other, competing spectrum agile networks and the protection of licensed radio networks are evaluated with the help of simulation. The efficiency of SLS for open spectrum access is demonstrated
Tawk, Y. et al, in “Reconfigurable Filtennas and MIMO in Cognitive Radio Applications” 2014 [53], the authors describe In this paper cognitive radio is described, discussed and compared with software defined radio (SDR). The two types of cognitive radio are presented and examples on both spectrum interweave and spectrum underlay cognitive radio antenna systems are detailed. Reconfigurable filtennas are proposed as communicating antennas in a MIMO setting for both cases of cognitive radio. The benefits of resorting to filtennas as well as to MIMO configuration is shown and discussed herein. The various antenna examples are designed, tested and compared with each other. Conclusions are drawn based on the presented results.
Brown, T.X. et al, in “Can Cognitive Radio Support Broadband Wireless Access?” 2007 [54], the authors describe One application of cognitive radios is to provide broadband wireless access (BWA) in the licensed TV bands on a secondary access basis. This concept is examined to see under what conditions BWA could be viable. Rural areas require long range communication which cognitive radios may not be able to support with enough secondary spectrum. Urban areas have less available spectrum and the BWA must use shorter ranges with greater spatial reuse. Furthermore, it is not clear what regulatory model would best support BWA. This paper considers demographic (urban, rural) and licensing (unlicensed, nonexclusive licensed, exclusive licensed) dimensions. A general BWA efficiency and economic analysis tool is developed and then example parameters corresponding to each of these regimes are derived. The results indicate that in rural areas an unlicensed model is viable and the additional spectrum would be useful despite existing unlicensed spectrum. In the densest urban areas the licensed models are not viable. This is not simply because there is less unused spectrum in urban areas. Urban area cognitive radios are constrained to short ranges and many broadband alternatives already exist. As a result the cost per subscriber is high. An unlicensed model is viable in urban areas, however the spectrum needs can be met with existing unlicensed spectrum. These results provide useful input for a variety of spectrum policy issues.
Jing Li et al, in “QoS provisioning multi-level spectrum allocation algorithm” 2010 [55], the authors describe Spectrum allocation is one of the key techniques of spectrum management in cognitive radio. In this paper, they introduce a new spectrum allocation algorithm named multi-level allocation algorithm based on cost minimized matching algorithm. Characteristics of both spectrum holes and cognitive users are taken into account in the new algorithm. Based on the statistical information of the characteristics, cognitive radio network can find out the optimal matching of spectrum holes to serve cognitive users. Performance analysis and computer simulations validate the efficiency of the proposed algorithm. The new algorithm outperforms the cost minimized matching algorithm with respect to system capacity and the expected number of spectrum handovers while provisioning user’s quality of service (QoS) requirements.
Yu Peng Xie et al, in “Spectrum Allocation Algorithm Based on Game Theory” 2011 [56], the authors describe Process of spectrum allocation is equivalent of rental market in cognitive radio, in order to rent free spectrum of authorized users to cognitive users. the lessors of Spectrum( between authorized users )use the methods of competition between authorized users, Lessees of Spectrum( between cognitive users ) also used the methods of game, the spectrum allocation between authorized user and cognitive users also have methods of game, which can be analyzed by game theory. Game theory can analyze lots of policy choice, but spectrum allocation in cognitive radio network is related to the problem of allocation choice, To make performance of the whole system optimal, spectrum allocation considers not only strategies of other users have an effect on their own, but also their own strategies cause interference to other users, to solve spectrum allocation in cognitive radio networks, game theory is in an effective way.
Haykin, Simon in “Cognitive Dynamic Systems: Radar, Control, and Radio [Point of View]” 2012 [57], the authors describe In this article on cognitive dynamic systems, the progress made and the way forward on this multidisciplinary integrative field has been reviewed. The following topics has been discussed: brief historical notes on human cognition; advances made on cognitive radar of the monostatic kind; emergence of cognitive control for the first time, building on the new concept of a two-state model and the significant progress made on cognitive radio on several fronts.
Jishun Liu et al, in “Demand-matching spectrum sharing game for non-cooperative cognitive radio network” 2009 [58], the authors describe In cognitive radio network, efficient spectrum sharing is one of the important functions which can enhance the overall spectrum utilization of whole network. However, it is still an unexplored issue to make unlicensed users choose the most suitable spectrum according to user demand and the spectrum characteristics. In this paper, a new demand-matching spectrum sharing (DMSS) algorithm based on game theory for non-cooperative cognitive radio network is proposed. Firstly, the model of the matching factors between the user demand and the spectrum characteristics is built up. Then by adopting demand-matching factor, the DMSS game is proposed and Nash equilibrium is solved by Nelder-Mead direct search method. DMSS enables each unlicensed user to access multiple appropriate channels by maximizing its own payoff. Numeral results reveal that DMSS achieves high performance in spectrum utilization percentage and spectrum efficiency of cognitive radio network when it reaches Nash equilibrium.
Pirmoradian, M. et al, in “Optimal spectrum hole selection & exploitation in cognitive radio networks” 2011 [59], the authors describe Future networks, especially in the framework of smart cities will be populated with various kinds of equipment accessing wireless communication channels. A much higher device variety will increase spectrum scarcity. Cognitive radio networks will be a key enabling technology in order to cope with the availability of the allocated radio spectrum bands. Cognitive Radio (CR) technology significantly utilizes current static spectrum bands assignment in an opportunistic manner. In this paper, they propose two spectrum opportunity (or spectrum hole) selection schemes; Minimum Collision Technique (MCT) and Maximum Residual Lifetime Technique (MRLT). The proposed techniques are evaluated by average channel utilization, average channel collision and successful secondary transmission bytes over licensed channels in a specific period of time (100s). The numerical results confirm that the MRLT scheme provides higher channel utilization and transmission bytes as well as decreases channel collision compared with the MCT scheme.
Pefkianakis, I. et al, in “SAMER: Spectrum Aware Mesh Routing in Cognitive Radio Networks” 2008 [60], the authors describe Cognitive radio technology holds great promises in enabling unlicensed operation in licensed bands, to meet the increasing demand for radio spectrum. The new open spectrum operation necessitates novel routing protocols to exploit the available spectrum opportunistically. In this paper they present SAMER, a routing solution for cognitive radio mesh networks. SAMER opportunistically routes traffic across paths with higher spectrum availability and quality via a new routing metric. It balances between long-term route stability and short-term opportunistic performance. SAMER builds a runtime forwarding mesh that is updated periodically and offers a set of candidate routes to the destination. The actual forwarding path opportunistically adapts to the dynamic spectrum conditions and exploits the link with the highest spectrum availability at the time. They evaluate SAMER through simulations, and show that it effectively exploits the available network spectrum and results in higher end-to-end performance.
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Chapter 3
Proposed Work
The Hidden Markov Model (HMM) is a popular statistical tool for modeling a wide range of time series data. The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. HMMs have found application in many areas interested in signal processing, and in particular speech processing.
Hidden Markov model: HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be considered as the simplest dynamic Bayesian network.
In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states. Note that the adjective ‘hidden’ refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still ‘hidden’.
Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition part-of-speech tagging, musical score following, partial discharges and bioinformatics.
3.1 Thesis Goals
The work aims to use Artificial Intelligence techniques for improving the scan time of Dynamic Spectrum access. It suggests to use Hidden Markov Model for modeling the spectrum behavior and predicting the spectrum pattern that will occur in future. This prediction is used to assist the DSA in improving its scan time. The algorithm for using HMM technique for reducing scan time of DSA will be implemeted in Matlab software. The algorithm takes as its inputs data sequence upto current time, t. Each data sequence represents a single snapshot of spectrum as a seuence of 0’s and 1’s. The spectrum is discretized in fixed sized bands. A 0 represents the band is free and 1 represents that the band is busy with a Primary User’s signal. The algorithm will predict the sequence that will be observed at t+1. This is given as input to the spectrum sensing algorithm based on FFT (Fast Fourier transform) implemented in Simulink software. The spectrum sensing algorithm will scan only those bands that have high probability of being free as evaluated by HMM. These bands are having 0’s in sequence output by HMM at t+1.
3.2 Tools and Techniques
For the above said research work, we will be requiring to access the various e-journals like IEEE, Science Direct and ACM. For data collection and analysis of the data, I will be requiring various software’s and Internet facility. The hardware and software requirements for my thesis work are
Hardware Requirements:
‘ Processor: Pentium IV or Higher
‘ RAM: 4 GB or Higher
‘ Hard Disk: 10 GB or Higher
Software Requirements:
‘ Platform: Windows 7 Professional
‘ MATLAB 2012b and above
‘ Microsoft office( Excel and Word)
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Chapter 4
Dynamic Spectrum Access
4.1 Introduction
The wireless spectrum is a freely available natural resource. In order for society to use this resource in a way that is productive, government agencies exist that regulate its use. The current system’s approach for spectrum access is to divide the spectrum into blocks. Licenses are issued by the government agencies for exclusive access, for a given geographical region, to these blocks. This system has worked well in the past, as it prevents multiple radios from interfering with each other without the need for coordination. However, this system does not allow dynamic sharing of the spectrum.
There have been some spectrum blocks reserved for use by any wireless device, without the need for a license (such as the Industry, Science, and Medicine Band). Such access has allowed wireless technology to become ubiquitous, by allowing rapid deployment of wireless devices without the overhead of obtaining a license. Also, for consumer wireless technology, such as Wireless Local Area Networks (WLAN), it is not feasible to license the spectrum.
Although unlicensed access to the spectrum is more flexible than licensed access, most of the current unlicensed access behaves similarly to licensed access. For example, most WLAN access points are deployed in a way such that they operate on mutually exclusive blocks of the unlicensed spectrum. Once deployed, the access points do not alter their behavior
dynamically, and thus cannot adapt to changing environment or to accommodate access by other unlicensed users.
4.2 Dynamic Spectrum Access Techniques
DSA techniques enable the efficient utilization of the radio spectrum by allowing access for unsubscribed SUs to the white spaces or spectrum holes in the radio spectrum
[3’5]. These white spaces or spectrum holes are defined as parts of the radio spectrum unutilized at a particular time and location [4]. The key difficulties in enabling SU access arise from the interference and possible distortion of service to the Primary Users (PUs), who can be considered as the subscribed users or the priority users in that spectrum band. The DSA policies allowing SU access to the temporarily unutilized part of the spectrum thus need to ensure safe, secure and un-interrupted access to the PUs [2’5].
This necessity of safeguarding the PU service requires the SU terminals to have a decision making capability based on DSA policies defined for their operation in the chosen spectrum band. Intelligent SU terminals aptly named as Cognitive Radios(CRs) have been envisioned to be the enabling technology for DSA.
Fig. 4.1 Dynamic spectrum access strategies
4.3 Dynamic Exclusive Use Model
This model maintains the basic structure of the current spectrum regulation policy: Spectrum bands are licensed to services for exclusive use. The main idea is to introduce flexibility to improve spectrum efficiency. Two approaches have been proposed under this model:
4.3.1 Spectrum property rights:
The former approach allows licensees to sell and trade spectrum and to freely choose technology. Economy and market will thus play a more important role in driving toward the most profitable use of this limited resource.
4.3.2 Dynamic spectrum allocation:
Dynamic spectrum allocation was brought forth by the European DRIVE project. It aims to improve spectrum efficiency through dynamic spectrum assignment by exploiting the spatial and temporal traffic statistics of different services. In other words, in a given region and at a given time, spectrum is allocated to services for exclusive use. This allocation however, varies at a much faster scale than the current policy.
4.4 Open Sharing Model:
This model employs open sharing among peer users as the basis for managing a spectral region. Advocates of this model draw support from the phenomenal success of wireless services operating in the unlicensed industrial, scientific, and medical (ISM) radio band (e.g., WiFi). Centralized and distributed spectrum sharing strategies have been initially investigated to address technological challenges under this spectrum management model.
4.5 Hierarchical Access Model:
This model adopts a hierarchical access structure with primary and secondary users. The basic idea is to open licensed spectrum to secondary users while limiting the interference perceived by primary users (licensees). Two approaches to spectrum sharing between primary and secondary users have been considered:-
4.6 Spectrum underlay:
The underlay approach imposes severe constraints on the transmission power of secondary users so that they operate below the noise floor of primary users. By spreading transmitted signals over a wide frequency band (UWB), secondary users can potentially achieve short-range high data rate with extremely low transmission power. Based on a worst-case assumption that primary users transmit all the time, this approach does not rely on detection and exploitation of spectrum white space.
Spectrum overlay:
It was the first envisioned under the term spectrum pooling and then investigated by the DARPA Next Generation (XG) program under the term opportunistic spectrum access. Differing from spectrum underlay, this approach does not necessarily impose severe restrictions on the transmission power of secondary users, but rather on when and where they may transmit. It directly targets at spatial and temporal spectrum white space by allowing secondary users to identify and exploit local and instantaneous spectrum availability in a non-intrusive manner.
4.7 Opportunistic Spectrum Access:
In this article, we focus on the overlay approach under the hierarchical access model. The term Opportunistic Spectrum Access (OSA) will be adopted throughout. Basic components of OSA include spectrum opportunity identification, spectrum opportunity exploitation, and regulatory policy. The opportunity identification module is responsible for accurately identifying and intelligently tracking idle frequency bands that are dynamic in both time and space. The opportunity exploitation module takes input from the opportunity identification module and decides whether and how a transmission should take place. The regulatory policy defines the basic etiquette for secondary users to ensure compatibility with legacy systems. The overall design objective of OSA is to provide sufficient benefit to secondary users while protecting spectrum licensees from interference. The tension between the secondary users’ desire for performance and the primary users’ need for protection dictates the interaction across opportunity identification, opportunity exploitation, and regulatory policy. The optimal design of OSA thus calls for a cross-layer approach that integrates signal processing and networking with regulatory policy making. In this article, we provide an overview of challenges and recent developments in both technological and regulatory aspects of OSA. The three basic components of OSA will be discussed in the following sections.
4.8 Spectrum Opportunity Identification
Spectrum opportunity identification is crucial to OSA in order to achieve nonintrusive communication. In this section, we identify basic functions of the opportunity identification module.
4.9 Spectrum Opportunity And Interference Constraint:
4.9.1 Spectrum Opportunity
Before discussing spectrum opportunity identification, a rigorous definition of spectrum opportunity is necessary. Intuitively, a channel can be considered as an opportunity if it is not currently used by primary users. In a network with geographically distributed primary transmitters and receivers, however, the concept of spectrum opportunity is more involved than it at first may appear [20].
With the help of Fig 4.1, we identify conditions for a channel to be considered as an opportunity. Consider a pair of secondary users where A is the transmitter and B its intended receiver. A channel is an opportunity to A and B if they can communicate successfully over this channel while limiting the interference to primary users below a prescribed level determined by the regulatory policy. This means that receiver B will not be affected by primary transmitters, and transmitter A will not interfere with primary receivers. To illustrate the above conditions, we consider monotonic and uniform signal attenuation and omnidirectional antennas. In this case, a channel is an opportunity to A and B if no primary users within a distance of rtx from A are receiving and no primary users within a distance of rrx from B are transmitting over this channel. Clearly, rtx is determined by the secondary users’ transmission power and the maximum allowable interference to primary users, while rrx is determined by the primary users’ transmission power and the secondary users’ interference tolerance. They are generally different. We make the following remarks regarding the above definition of spectrum opportunity.
Spectrum opportunity is a local concept defined with respect to a particular pair of secondary users. It depends on the location of not only the secondary transmitter but also the secondary receiver. For multicast and broadcast, spectrum opportunity is open for interpretation, and results in networking tradeoffs. Spectrum opportunity is determined by the communication activities of primary users rather than that of secondary users. Failed communications caused by collisions among secondary users do not disqualify a channel from being an opportunity.
4.10 Interference Constraint
How to impose interference constraints is a complex regulatory issue. Restrictive constraints may marginalize the potential gain of OSA, while loose constraints may affect the compatibility with legacy systems. Generally speaking, an interference constraint should implicitly or explicitly specify at least two parameters: The maximum interference power level ?? perceived by an active primary receiver and the maximum probability ?? that the interference level at an active primary receiver may exceed ?? [20]. The first parameter, ??, can be considered as specifying the noise floor of primary users; interference below ?? does not affect primary users, while interference above ?? results in a collision. It is thus inherent to the definition of spectrum opportunity and determines the transmission power of secondary users.
The second parameter, ??, specifies the maximum allowable collision probability. Given that errors in spectrum opportunity detection are inevitable, a positive value of ?? is necessary for secondary users to ever be able to exploit an opportunity. As discussed later, ?? determines a secondary transmitter’s access decision based on imperfect spectrum opportunity detection. A cautionary aspect is that different definitions of collision probability offer different levels of protection to primary users [20]. For example, the collision constraint can be imposed on the joint probability that both primary and secondary users access the same channel or on the conditional probability that a secondary user transmits given that the channel is occupied by primary users. The protection offered by the former constraint varies with the traffic load of primary users; primary users with a light traffic load may not be as well protected as those with a heavy traffic load. Another issue is whether the constraint should be imposed in each slot over each channel or on the collision probability averaged over channels and a long period of time.
The former offers a specific level of protection to primary users no matter when and over which channel they transmit, while the protection given by the latter can be unpredictable when primary users have burst arrivals of short messages.
While an interference constraint specified by {??, ?? } should be imposed on the aggregated transmission activities of all secondary users, each secondary user needs to know the node-level constraint in order to choose transmission power and make access decisions. The translation from a network level interference constraint to a node-level one depends on the geo-location and traffic of secondary users as well as the signal attenuation model in the communication environment with shadowing and fading.
4.11 Parametric Adaptive Spectrum Sensing Architecture
The PASS architecture incorporates the various mechanisms for primary signal detection, collaborative spectrum sensing, fine tuning of spectrum sensing parameters, and scheduling of spectrum sensing. With the help of a priori information, such as the knowledge gained from the licensing database, as well as from real time spectrum monitoring, the proposed PASS architecture enables the radio to adapt its spectrum sensing mechanism to variations in the spectrum operating conditions. The block diagram for the proposed PASS architecture is shown in Fig4.2.The RF front end functions both as the passive spectrum sensor as well as the transceiver for radio communication. While functioning as the spectrum sensor, the RF front end is responsible for sampling the channel occupancy and collecting spectrum measurements. The spectrum sensing control mechanism controls the parameters of the sensing operation such as sweep bandwidth Bs whose limits are specified by Fstart And Fstop, bandwidth resolution Br, binwidth Bd, dwell time Td, sweep time Ts, and sweep time resolution Tr[21],[23] . The spectrum utilization can be measured along the frequency dimension, time dimension, spatial extent, and signal format. However, in this paper we only consider the measurement of the spectrum along its frequency and time dimensions. Accordingly, a set of spectrum measurements can be represented as an Nt?? Nf matrix, M, defined as [26]:
Fig 4.2 Block Diagram of proposed Parametric Adaptive Spectrum Sensing Architecture
M = [M(fi, tj)],
Where Fstart ‘ fi < Fstop, Tstart’ tj<Tstop, i = 1,’.’.Nf, j= 1, . . .Nt, given that M(fi, tj) is a measurement sample of the RF power(expressed in dBm) at frequency channel fi and time instance tj, Fstart and Fstop specify the start and stop frequencies for the measurement sweep, Tstart and Tstop specify the start and stop time instances for the sweep, while Ntand Nf are the number of time instances and the number of frequency channels for which the measurements are collected. A database of spectrum measurements that are collected by the radio itself or obtained from other radios [22] is maintained by the data management and processing (DMP) block. The spectrum data from the database is retrieved and processed in order toper form signal detection. In the characterization and learning block, the processed spectrum data is analyzed in order to characterize the radio’s operating spectrum. The radio learns about the characteristics of its wireless operating environment in two stages. Initial learning is performed after the radio has been deployed and before it becomes fully-functional. During the initial learning stage, the occupancy model parameters are estimated from the measurement data and when the estimates of the model parameters reach a certain confidence level, the initial learning stops. At this point, the radio becomes fully-functional and the stage of simultaneous learning begins. In this learning stage, Spectrum Available frequency channel Occupancy Fig 4.3 Block Diagram Showing sequence of Operation in PASS Architecture The RF front end is shared between the sensing mechanism and the radio communication subsystem, and the learning process occurs simultaneously along with the primary operations of the radio. The opportunistic spectrum usage (OSU) control block chooses a set of frequency channels among a pool of unoccupied channels for performing the ‘listen-before-talk’ function of the cognitive radio. The OSU control block makes these decisions based on the current wireless channel characteristics and channel availability statistics which have been determined by processing and analyzing the spectrum data. Accordingly, it also decides the time-frequency assignments for the sensing operations and radio communication operations. The time frequency code kernel block stores these assignments which indicate how the RF front end resources are shared among the two operations. After every transmission, the learning block is notified by the kernel so that the model parameters are updated. ‘ Chapter 5 Hidden Markov Model 5.1 Introduction The Hidden Markov Model (HMM) is a popular statistical tool for modeling a wide range of time series data. The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. HMMs have found application in many areas interested in signal processing, and in particular speech processing. Hidden Markov model: HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be considered as the simplest dynamic Bayesian network. In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states. Note that the adjective ‘hidden’ refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still ‘hidden’. Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition part-of-speech tagging, musical score following, partial discharges and bioinformatics. 5.2 Markov Processes fig 5.1 depicts an example of a Markov process. The model presented describes a simple model for a stock market index. The model has three states, Bull, Bear and Even, and three index observations up, down, unchanged. The model is a finite state automaton, with probabilistic transitions between states. Given a sequence of observations, example: up-down-down we can easily verify that the state sequence that produced those observations was: Bull-Bear-Bear, and the probability of the sequence is simply the product of the transitions, in this case 0.2 ?? 0.3 ?? 0.3. Hidden Markov Models Fig 5.2 shows an example of how the previous model can be extended into a HMM. The new model now allows all observation symbols to be emitted from each state with a finite probability. This change makes the model much more expressive Fig 5.1 Markov Process example Fig 5.2 HMM example and able to better represent our intuition, in this case, that a bull market would have both good days and bad days, but there would be more good ones. The key difference is that now if we have the observation sequence up-down-down then we cannot say exactly what state sequence produced these observations and thus the state sequence is ‘hidden’. We can however calculate the probability that the model produced the sequence, as well as which state sequence was most likely to have produced the observations. The next three sections describe the common calculations that we would like to be able to perform on a HMM. ‘ The formal definition of a HMM is as follows: ?? = (A, B, ??) (4.1) S is our state alphabet set, and V is the observation alphabet set: S = (s1, s2 , ‘ ‘ ‘ , sN ) (5.2) V = (v1 , v2 , ‘ ‘ ‘ , vM ) (5.3) We define Q to be a fixed state sequence of length T, and corresponding observations O: Q = q1, q2 , ‘ ‘ ‘ , qT (5.4) O = o1, o2, ‘ ‘ ‘, oT (5.5) A is a transition array, storing the probability of state j following state i. Note the state transition probabilities are independent of time: A = [aij ] , aij = P (qt = sj |qt’1 = si ) . (5.6) B is the observation array, storing the probability of observation k being produced from the state j, independent of t: B = [bi (k)] , bi (k) = P (xt = vk |qt = si ) . (5.7) ?? is the initial probability array: ?? = [??i ] , ??i = P (q1 = si ) . (5.8) Two assumptions are made by the model. The first, called the Markov assumption, states that the current state is dependent only on the previous state, this represents the memory of the model: (5.9) The independence assumption states that the output observation at time t is dependent only on the current state, it is independent of previous observations and states: (5.10) 5.3 Evaluation Given a HMM, and a sequence of observations, we’d like to be able to compute P (O|??), the probability of the observation sequence given a model. This problem could be viewed as one of evaluating how well a model predicts a given observation sequence, and thus allow us to choose the most appropriate model from a set. The probability of the observations O for a specific state sequence Q is: P (O|Q, ??) = = bq1 (o1 ) ?? bq2 (o2 ) ‘ ‘ ‘ bqT (oT ) (5.11) and the probability of the state sequence is: P (Q|??) = ??q1 aq1 q2 aq2 q3 ‘ ‘ ‘ aqT ‘1 qT (5.12) so we can calculate the probability of the observations given the model as: (5.13) This result allows the evaluation of the probability of O, but to evaluate it directly would be exponential in T. A better approach is to recognize that many redundant calculations would be made by directly evaluating equation 5.13, and therefore caching calculations can lead to reduced complexity. We implement the cache as a trellis of states at each time step, calculating the cached valued (called ??) for each state as a sum over all states at the previous time step. ?? is the probability of the partial observation sequence o1 , o2 ‘ ‘ ‘ ot and state si at time t. This can be visualized as in figure 5.3. We define the forward probability variable: ??t (i) = P (o1, o2 ‘ ‘ ‘ ot , qt = si |??) (5.14) so if we work through the trellis filling in the values
of ?? the sum of the final column of the trellis will equal the probability of the observation sequence .The algorithm for this process is called the forward algorithm and is as follows: Initialization: (5.15) 2. Induction: (5.16) 3. Termination (5.17) Fig. 5.3 The induction step of the forward algorithm The induction step is the key to the forward algorithm and is depicted in fig 5.3. For each state sj,??j(t) stores the probability of arriving in that state having observed the observation sequence up until time t. It is apparent that by caching ?? values the forward algorithm reduces the complexity of calculations involved to N2T rather than 2TNT. We can also define an analogous backwards algorithm which is the exact reverse of the forwards algorithm with the backwards variable: (5.18) as the probability of the partial observation sequence from t + 1 to T , starting in state si. 5.4 Markov chain Modeling of True States and it Validation The sequence Y is modeled as a Markov chain, which is characterized by an initial distribution ??= (p0, p1) and one- step transition matrix This means that is a Markov chain with state space S = (0, 1) the distribution of y1 is ?? and (5.19) , = For every and These stipulations give the joint distribution of and is expressed as: (5.20) For all 5.5 Markov Model of Spectrum Occupancy The dynamics of Sfi can be characterized by a two state markov chain, which is shown in Fig. 5.4. The parameters of the markov model are the state probabilities denoted by P0 and P1 corresponding to the states 0 and 1 and the state transition probabilities P00, P01, P10, and P11. In the previous works [24], in building a markov model for the channel occupancy, it was assumed that the channel occupancy is stationary. However in practice, the model parameters may Fig 5.4 A simple Markov model for channel occupancy states. Be time varying. For instance, the usage of a cellular mobile channel varies drastically with the time of the day with the peak usage being during the business hours. A weighted approach has to employ in order to compute the model parameters, where more weight is given to the current and most recent instantaneous channel states. For a particular frequency channel fi and at a particular time instance tk, the weighted count of past occurrences of state 1 is determined from Mc and is denoted by Nw (Mc(fi, tj) = 1) and the corresponding weighted state probability is defined by: (5.21 ) (5.22 ) Where The weights are given based on the time of occurrence of the channel state relative to tk. The variable tk is a discrete time variable, where the total number of measurement samples collected from time tj= 0 to (tk ‘1) is denoted by Ntk. The estimate of the true probability is computed from the finite set of spectrum measurement samples using: (5.23) where ?? is the forgetting factor3 for 0 ‘ ‘?? 1. Similarly, the weighted probability of transitions from state 0 to state 1 in channel fi is computed as: (5.24) Where , such that the term in the numerator represents the weighted number of occurrences of transitions from state 0 to state 1 in channel fi. In the same manner, the other transition probabilities and model parameters can be computed. The time taken to estimate a statistically accurate markov model has to be kept minimum so that the learning and characterization period of the cognitive radio is small. There arises a question on how many measurement samples the radio needs to estimate the parameters of the model, given finite measurement data and the limitations of the spectrum sensing mechanism. Before deriving the sampling distribution for the model parameters, we make a reasonable assumption that the channel occupancy is piecewise stationary. For instance, during the non-peak hours on a normal weekday, the channel occupancy in a cellular mobile band is less and remains approximately stationary .We base our discussion over a segment of time when the channel occupancy is stationary. We also assume that the random variable Sfi is ergodic and that the measurement samples are observed independently and under similar experimental conditions. With these assumptions, the Ntk measurement samples that are collected from the frequency channel fi can be viewed as constituting the sample space of Sfi. The time spacing, ??t, between the samples is proportional to the sweep time resolution of the sensing mechanism. If T is the total time duration for which the measurement samples are collected then T = Ntk. ??t. If the resolution is improved then the number of samples Ntk is increased. Consider the following expression for estimation of the true probability for the case of the stationary channel occupancy: (5.25) In Eq. (5.25), Mc(fi, tj) represents samples of the two state random variable Sfi and the summation of the samples, which is the number of instances when the channel is occupied, follows a binomial distribution B(Ntk , ). It is also seen that, the probability is actually the mean occupancy in the frequency channel fi, i.e. is a statistic of Sfi. is a consistent estimator of and it can be treated as a random variable with a certain sampling distribution. For large Ntk (Ntk > 10), the binomial distribution can be approximated with a normal distribution and
can be treated as a Gaussian random variable with normalized form denoted as z’ N(0,1).
If ?? is the confidence coefficient, we can denote the 100?? percentage point by z?? [25]. Let tn be the corresponding random variable with a student t distribution of n (n = N’1) degrees of freedom and its 100?? percentage point is denoted as tn,??. In the derivation of the sampling distribution, we make an assumption that the samples of the channel occupancy are not correlated with each other and this assumption can be met by sampling the channel occupancy at random intervals as described in [24]. From the sampling distribution of , the confidence interval for can be defined as [25]:
< (5.26)
It is seen from Eq. (5.26) that, for a given degree of uncertainty, as the number of samples Ntk increases the confidence interval narrows and the accuracy of the estimate increases. Having estimated the Markov model parameters for every frequency channel in the spectrum bandwidth of interest, the sensing parameters such as the time resolution can be set
adaptively according to the channel occupancy.
Chapter 6
Methodology
6.1 Integrating HMM with DSA
The configuration of the proposed spectrum sensing model for a specific sub-band .The same model can be applied for several sub-bands within the operating spectrum. Power measurements are collected for a sub-band at regular time intervals (in seconds) spanning over an observation period (typically, hundreds of seconds). These measurements are translated into binary occupancy data Y that serves as the historic data for offline reference by the CR as shown in Figure 1. Based on the retrieved measurements, the CR is trained to perform a validation check to ensure markovian property of the spectrum occupancy by PUs in the sub-band under consideration. If the sub-band occupancy follows a Markov chain, the markovian parameters are estimated. The CR also simultaneously senses the spectrum for PU occupancy and passes this information X to the HMM block and is used in generating the predicted results by the forward backward algorithm. This predicted output X’, as well as the output X generated by the CR, can now be compared with the actual PU occupancy Y to scrutinize the accuracy of the proposed prediction mechanism.
We define a observation period ?? = {1,2,”.?? } where each i in ?? represents the ith sensing duration.We also define a sequence Y = {y1,y2”y??}.
Free band with highest probability
Fig 6.1 System model for efficient spectrum sensing
SU Belief Prob.[PU idle at t ]
Action Smaller time scale
than PU traffic
Observation IDLE/BUSY Ack/Nack
Belief update
Fig 6.2 System Model: SU Access
represents the true states in the corresponding time periods. The entity yi=1 if the sub-band is free at the ith time instant and yi=0 otherwise. The CR output generated by a sensing mechanism is represented by a sequence X = {x1,”.x??} of sensed states in the corresponding time periods. The entity xi = 1 if the state of the sub-band is sensed to be free at the ith sensing slot and xi = 0 otherwise. The sequence X represents the prediction of the true state sequence Y = {y1”y??}.In reality, the true state sequence Y is unobservable. Collecting real-time measurements is time-consuming and expensive when considering an extensively wide spectrum over a period of weeks and months.
6.2 Details of integrating DSA with HMM
6.2.1 Processing of Spectrum Sensing Data
The spectrum occupancy of a specific frequency channel fi can be modeled as a two state random variable denoted by Sfi. The two states are ‘occupied’ (i.e. signal present) and ‘available’ (i.e. signal absent). The measurement data is processed and classified before being analyzed. Classification of the spectrum measurements involves identifying the channel occupancy state by processing the measurements. For instance, if a spectrum measurement M(fi, tj) is classified as a signal, we can infer that the channel fi was in the ‘occupied’ state at time instance tj . However, the channel state can sometimes be classified as ‘uncertain’. For instance, when the radio is located in a region of a deep fade and the power sensed by it may not be high enough to be classified as signal power nor can it be classified as noise. A measurement sample M(fi, tj) is classified as a signal (represented as ‘1’), noise (represented as ‘0’), or uncertain (represented as ‘x’) state based on two decision thresholds ??u and ??l, as shown by:
=
where Mc(fi,tj) represents the occupancy state of a channel fi at time instance tj and is an element of the matrix of classified measurements, denoted by Mc.
Fig.6.3 Markov decision process to resolve uncertainty in channel state
The ‘uncertain’ state can be resolved, with the help of additional information or collaboration with other radios in the DSA network. In the case of collaborative sensing, the radios which are not present in any deep fade can classify the channel power measurements correctly and pass this information to the radios that are in a deep fade [22]. We present an alternative method of resolving the uncertainty in the channel states. This technique does not require any cooperation from the other radios and it may be used in conjunction with collaborative spectrum sensing approaches. In this method, the present ‘uncertain’ channel state is resolved based on the observed previous channel states. Fig. 5 illustrates the decision process involved in resolving the uncertainty in the channel state with the help of the previous state of that channel. The decision may also be based on observing the channel state over a specified period of time rather than the previous state only. If the previous channel state was ‘occupied’ and the current channel state is ‘uncertain’, then the current state is resolved as ‘occupied’. Similarly, if the previous state was ‘available’ then the current uncertain state is also resolved as ‘available’. Finally, using the matrix M in conjunction with Mc, information that will be useful in characterizing and modeling the spectrum occupancy can be extracted. The next section describes a statistical approach to efficiently build markov models which can be used to model both stationary as well as non-stationary spectrum occupancies.
6.3. Hidden Markov Model Algorithm
HMM consists of <S, s0, O, A, B>
‘ S : the set of states
‘ so: the initial state where everything begins
O : the sequence of observations, each of which is drawn
from a vocabulary, <o1, o2, …, oT>
A : the transitional probability matrix
B : the emission probabilities, where bi(oT) is the
probability of observation ot generated from state i
If we Know the state sequence
5.1.1 Optimized Algorithm for Reducing Scan time
1. Initialize the spectrum band to some white space
2. Calculate ‘HMM’ probabilities based on the current band
3. Use the ‘Optimum band’ probabilities to estimate the expected frequencies
Expected number of transitions from state i (to state j)
Expected number of being in state j (and observing )
Expected number of starting in state j
4. Sense the expected frequencies to estimate the parameters from observation state
{ }
5. Reduce the band size by 1 at time instant t+1
6. If Band size reach to 1 Repeat 2-4 until the optimum band is not find that is no longer to use.
Basic idea: SU allocate channel only if PU is detected to be inactive
No
Yes
Fig 6.4 Flow Chart
Chapter 7
Results And Conclusion
7.1 Simulation Results and Conclusion
The algorithm for using HMM technique for reducing scan time of DSA has been implemeted in Matlab software. The algorithm takes as its inputs data sequence upto current time, t. Each data sequence represents a single snapshot of spectrum as a seuence of 0’s and 1’s. The spectrum is discretized in fixed sized bands. A 0 represents the band is free and 1 represents that the band is busy with a Primary User’s signal. The algorithm predicts the sequence that will be observed at t+1. This is given as input to the spectrum sensing algorithm based on FFT (Fast Fourier transform) implemented in Simulink software. The spectrum sensing algorithm will scan only those bands that have high probability of being free as evaluated by HMM. These bands are having 0’s in sequence output by HMM at t+1.
As the bands now being searched by Spectrum Sensing algorithm are less in number the algorithm will finish the scanning in less time. The scan results of spectrum sensing algorithm giving spectrum snapshots will become input to the next iteration of HMM.
The number of bands being scanned spectrum sensing algortihm will keep on decreasing and when it reaches a minimum count, the HMM is restarted with spctrum snapshot of complete band. This results in a saw-tooth shaped plot of scan time of DSA with time as shown in (Fig. 7.1-Fig 7.3).
Fig 7.1 Time interval vs search time
Another plot shown in Figure-(figure no.) shows the comprision between spectrum access without hmm and with hmm in term of number of free holes and scan time for searching a free holes in microseconds. This graph shows the how the suggested HMM based DSA technique improves over the DSA technique without HMM. The scan time shown for HMM based DSA is the evaluated by taking the average of scan time values over a time duration.
Fig 7.2 No. of holes vs scan time
Fig 7.3 Time index vs probability
7.2 Conclusion
The thesis proposed an approach based on HMM for reducing the scan time of dynamic spectrum access in cognitive radio. The simulation results show that the approach suggested does improve the scan time of DSA. This approach is restricted in terms of the size of new snapshots of DSA that keeps on decreasing as the algorithm progresses and needs to start again from scanning the complete band. The work can be further extended by designing an approach removes this restriction and thus leading to more efficient use of spectrum.
‘
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Essay: OPPORTUNISTIC SPECTRUM ACCESS USING HIDDEN MARKOV MODELS IN DSA
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