Essay: MIMO system

LITERATURE REVIEW
 
Nowadays, the high-speed cellular network has grown rapidly. This high-speed cellular network develops as the number of users increases. The increasing number of users will cause high demand and large data-rate requirement may be needed. The multiple-input multiple-output (MIMO) has been developed in promising better spatial diversity and system capacity. Basically, this technique refers to a method of multiplying a radio link capacity by implementing multiple antennas at transmitter and receiver, which is applied for exploiting the multipath propagation. The MIMO system has become an essential method that uses wireless communication technologies such as High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE).
Over the same radio channel, this MIMO technique is used to send and receive more than one data signal simultaneously through exploiting the multipath propagation. Basically, between the transmitter and receiver, there are many paths that can be taken by signal. The path will be changed if the transmitter, receiver or any object in the channel moves. The number of objects that appears either on the side or direct path between transmitter and receiver will produce a variety of paths. However, the interference is produced between these multiple paths. Thus, the usage of MIMO system will give advantages to these multiple paths. This is possible by providing more robustness to the radio link, where the link data capacity increases or the noise ratio improves. There are two main formats for MIMO systems which are spatial diversity and multiplexing diversity. The spatial diversity refers to transmit and receive diversity. The spatial diversity employs multiple antennas with same characteristic and it allows a limited communication spectrum to be shared by multiple users. For multiplexing diversity, this form can be used to provide an additional data capacity. The additional data capacity can be produced by utilizing the multiple paths to carry additional traffics. The general outline of MIMO system is shown in Figure 2.1.
 
Figure 2. 1: General outline of MIMO system
 
However, the realization of MIMO technique is limited over the uplinks transmission because of high cost and the user equipment size. Hence, to overcome this MIMO limitation, the virtual-MIMO (V-MIMO) is presented. The V-MIMO system has been used in various systems such as ultra-wide band (UWB) by Srivastava & Mohan (2016) and wireless networks by Li et al. (2016). V-MIMO is constructed by a number of individual user equipment (UE), where each UE has a single transmitter antenna. Then, the data stream is being transmitted independently either on the same subcarrier or sub-channel. In the uplink transmission, the V-MIMO can be constructed by ensuring the number of receiver antennas should be at least the same as the number of transmitting antennas. The user pairing is one of the important processes in V-MIMO system, in which different users with single-antennas are selected to constitute a V-MIMO. By implementing user pairing, the multi-user diversity can be achieved. However, when more antennas are presented either at transmitter or receiver, less multi-user diversity will occur. Hence, it is essential to have a scheduling criteria when performing user pairing in V-MIMO such as random pairing scheduler (RPS), proportional fair (PF) or round robin (RR) by Chen et al. (2008). The general outline of the V-MIMO is presented in Figure 2.2.
 
Figure 2. 2: General outline of V-MIMO system
 
2.1 User Grouping or User Pairing
User pairing approach has been done by Chao et al. (2007). The Adjustable Determinant Pairing Scheduling (ADPS) is proposed as scheduler criterion when performing the user paring. The proposed scheme is developed to overcome the weakness of traditional algorithm for user paring. ADPS considers fast fading and slow fading, but the traditional algorithm only considers slow fading. Hence, the maximum throughput is difficult to achieve. The authors consider both slow fading and fast fading in their research work. The analysis has been made to compare the proposed algorithm with the random pairing scheduling (RPS) and determinant pairing scheduling (DPS). The random pair scheduling (RPS) has been used in V-MIMO system for pairing user into a group. Basically, the first user is selected by using the pairing algorithm such as round-robin (RR). For the second user selection, this method will select the second users randomly from the remaining set of users. This algorithm can perform the user pairing with lower computational complexity, but it is not fit to maximize the system throughput. For DPS method, this pairing method only takes into account for the fast fading and not for slow fading. Thus, this method cannot achieve higher throughput. An adjustable factor has been introduced in the proposed scheme, where this adjustable factor is used to adjust the influence of SNR for each user equipment pairing. The ADPS algorithm begins with measuring the SNR value for each user equipment (UE), where this measurement is done by the base station. The channel matrix between the base station and user equipment is measured as well by the base station. Next, the first user will be selected by base station and the second user is selected from the remaining user equipment. The second user is selected by calculating the paring factor with the first user. The criterion for the second user to be selected is depending on the pairing factor. Hence, on the remaining users, the users that achieve the maximum pairing factor is chosen to be paired with the first user. However, the authors only focus on presenting the pairing algorithm with two users. Because the second user will be selected from the remaining user equipment, high computational complexity occurs when more than two users are paired. This is because a single calculation is made for each user equipment in determining the pairing factor before they can be paired.
Another study on the user pairing algorithms has been done by Chen et al. (2008). The uplinks virtual-MIMO (V-MIMO) is considered in this research work, where the double proportional fairness (D-PF) algorithm is proposed for implementing the user paring. For user pairing, the first user is selected using the proposed D-PF algorithm with proportional fairness criterion. Then, the second user is chosen, where second user is selected by implementing a modified proportional fair criterion. The performance comparison has been made between the proposed D-PF algorithm with the single PF (S-PF). The S-PF will choose the first user using the proportional fair criterion. Then, the next user is selected to maximize the overall throughput of the user pair. Tough the S-PF algorithm is able to achieve a high overall throughput, but a good fairness between users may not being offered. The scheduler on the base station will choose the group to share the time-frequency resource blocks. In this study, the author focuses only on implementing the user paring with two users for every group, hence, the number of paired users is fixed to two. However, the proposed D-PF algorithm is able to be implemented with more users per group, where the additional number of receiver antennas is needed. For the uplinks V-MIMO system, the identical resource blocks are shared by at least two users or more, in order to improve the system spectral efficiency. By comparing the performance of the proposed D-PF algorithm with the single PF (S-PF), the proposed D-PF algorithms achieve fairly good performances on the trade-off between fairness and throughput.
The capacity for the uplink multiuser system can be enhanced by implementing the multiuser scheduling as shown by Viterbo and Hottinen (2008). A scheduling strategy is introduced where the users transmit in different time, frequency or code slots and accordingly refer to the channel quality. The proposed scheduling strategy will be used to solve the problem of combinatorial optimization. The Hungarian algorithm is proposed to perform the multiuser scheduling with the Minimum Mean Square Error (MMSE) receiver detection. It is possible to improve the capacity of the multiuser system in different time-frequency-code (TFC) slots because each user will use different channel condition. The capacity can be enhanced by allocating different groups of users. The user pairing starts by assuming the number of paired users that will access two TFC slots in order to double their rate. Then, the single user will use only one TFC slot as a compensation and allow to double the transmit power. A higher modulation can be employed by the unpaired users by transmitting the double transmit power. Hence, their spectral efficiency can be doubled and compensated for their usage of one TFC slot only. The proposed strategy has improved the performance of multiuser system, where the strategies of efficiently exploiting the channel, interference diversity and joint optimisation have been utilised to improve the multiuse system.
The effort for improving the throughput and users fairness has been done by Han, Tao, and Cui (2009), where a user pairing method is introduced for the V-MIMO on the third generation (3G) Long Term Evolution (LTE). The spatial diversity can be rendered by taking into account the large scale and small scale fading. The pairing algorithm which is based on the simplified SNIR-based pairing scheduling (SSNIR-PS) has been proposed to implement the user pairing and improve the fairness of the paired users. The proposed pairing scheduling is aimed to approach the near-optimal throughput performance with lower computational complexity. By assuming two receiver antennas are presented at based station, the user pairing starts by choosing the first user equipment. The first user equipment is selected by performing the scheduling algorithm which is round-robin. Then, the next user equipment that would be paired with the selected first user equipment is based on different criteria. A parameter is set to ensure the relatively fair performance of the selected users in which this parameter considers the large SNIR with the narrowest SNIR gap between paired users. Thus, the user pair that achieves the less difference in SNIR will be selected by the base station. Basically, the user pair is based on the SNIR for each user. The SNIR is chosen as an accurate parameter because it is related to the detection algorithm at the receiver. This research work considers 2×2 V-MIMO system, thus, only two users are considered in each group. It is not guaranteed that the proposed scheme is efficient for user pairing because only two users and one group are presented. The discussion on more than two users to be paired using the proposed algorithm is not covered.
An effort to achieve a good, balanced performance between the user fairness and system capacity has been discussed in Su et al. (2010). A novel multiuser pairing scheduling is proposed based on the proportional fair scheduling. The proposed scheduling scheme is presented as the Quality of Service (QoS), and the proposed scheduling is presented as (PF-QoS). The proportional fair is selected due to its ability to obtain a balanced performance between maintaining the user fairness and maximizing the system capacity. By providing high data rate, the proportional fairness (PF) will assign the resource to the user and the fairness of user is guaranteed on the average data rate. However, the effort of the multiuser paring process is not discussed in detail. The criteria for each user to be paired remain unclear.
A novel algorithm for user-pairing has been introduced by Dhakal & Kim (2010), in which the proposed algorithm is presented for the V-MIMO system. The expression for the output SNR (OSNR) in a V-MIMO system has been derived by utilizing the channel orthogonality. While, the proposed orthogonal user pairing is presented to maximize the average system throughput performance, the computational complexity for the proposed algorithm is based on the independent number of user. The computational complexity tends to reduce when the number of receiver antennas increases. For user pairing methods, the random pairing, orthogonal pairing, capacity crossover pairing and maximal capacity pairing have been discussed. However, this study does not take into account the problems of user pairing and frequency allocation.
A user pairing transmission scheme in the uplink Coordinated Multi-point (CoMP) has been proposed by Li et al. (2010). In LTE networks, CoMP has been adopted as one of the promising concepts in increasing the cell edge user data rate and total sector throughput for both uplink and downlink transmission. The inter-cell interference can be mitigated by applying this CoMP concept. In order to explore the space dimension, user pairing is an efficient technique that can be considered. In addition, the system capacity as well as special efficiency can be improved by utilizing the user scheduling technique. The uplink user pairing scheme has been introduced for CoMP by utilizing the time or frequency resources, where it is used to improve the system capacity and spectrum efficiency. By exploring the space dimension on the uplink transmission, the user pairing scheme is able to improve the spectrum efficiency and user throughput. Two users are considered to be paired and form a V-MIMO concept, while two receiver antennas are presented at base station. The pairing scheme starts by selecting the first user equipment (UE). The round-robin (RR) scheme is selected to be implemented for selecting the first UE. Then, the second UE to be paired is selected by implementing different schemes, where the selected schemes are random pairing (PR) scheme, orthogonal pairing (OP) scheme and determinant pairing (DP) scheme. For PR scheme, the second UE is selected randomly. This scheme seems easy and flexible to be implemented with low computational complexity. However, the PR scheme is not able to achieve the maximum cell throughput because the channel information is not used in this scheme. Thus, this scheme will cause serious interference and the resource cannot be utilized effectively. Next, for the OP scheme, the user equipment pair is selected by the maximum orthogonal to the first user equipment. Then, the second user equipment is selected by maximizing the numerator criterion. The DP scheme is quite similar to the OP scheme with one exception, the calculation of the numerator criterion. Utilizing both OP and DP schemes in grouping the users will allow the reduction of the interference between the users. The proposed scheme does not guarantee that this scheme is effective for more than two users per group. The third user selection will be more complex because the first user is selected based on the round-robin method and second user is selected based on either the RP, OP or DP scheme.
A joint user pairing using a precoding scheme for the multiuser MIMO (MU- MIMO) has been developed by Xia et al. (2010). The SNIR is chosen as an objective function, with each base station selects the users with the highest SINR to be the first user. A unitary matrix is generated based on the first user precoding vector from which the base station selects the users one by one. This scheme achieves a higher sum rate when compared to the signal to leakage and noise ratio (SLNR) algorithm.
The problem of partitioning the transmitter nodes for creating cooperative groups has been studied by Hong et al. (2013). Joint user grouping is implemented by defining a subset of nodes for a large number of users. A technique called semi definite relaxation (SDR) is used for the joint optimisation problem where each user is considered a single transmitter antenna. Two network scenarios are tested. The first uses the cooperation of single multiple transmit antennas for transmitting the data to a single receiver antenna. The second, with the help of the cooperative relays, uses a single transmitter antenna to transmit the data to the receiver antenna.
In heterogeneous mobile networks, the user association and resource allocation efforts have been studied by Fooladivanda & Rosenberg (2013). Proportional fairness (PF) is selected as the objective function in managing user association and three different channel allocation schemes are considered. The performance analysis shows that different user combinations have different throughput performances, hence, significant gains can be realized with an accurate combination of user association and resource allocation. Unfortunately, the user scheduling cannot be evaluated, as only the flat channel assumption is made.
User grouping in the context of minimizing the power in MC-CDMA has been introduced by Phasouliotis et al. (2011). This optimisation is affected by whether the user grouping operates with or without fairness criteria. The total transmitted power can be minimized when no fairness criteria is active by allowing only users with high gains to use the available sub-carrier. A greedy algorithm is used for user grouping and power allocation and operated in the context of fairness criteria and without it.
The user grouping for OFDMA-IDMA has been investigated by both Dang et al. (2013) and Zhou et al. (2013). In Dang et al. (2013), a sub-optimal algorithm is proposed by formulating the user grouping problem into integer linear programming (ILP). For grouping the users, the users and sub-carriers are divided into a number of groups. Each sub-carrier is allocated for an exclusive use of the selected group of users. Through this effort, the complexity of the total decoding is reduced while maintaining the overall system performance. In Zhou et al. (2013), the objective function for user grouping is set to maximize the capacity. The users are distributed into the selected sub-carriers, based on the prevailing channel conditions. For grouping users, a near-optimal algorithm is designed for the interleaved and localized sub-carrier allocation. The concept of many to one matching and one to one matching is introduced for grouping the users.
User grouping for multiuser MIMO has been studied by Chen et al. (2012). A combined block diagonal and vector perturbation (BD-VP) algorithm is proposed for grouping the users, with the vector perturbation improves the capacity performance. The precoder performs both the traditional BD-VP and low complexity BD-VP. The scheme is introduced as a sub-optimal algorithm for minimizing the total transmitted power. However, this effort focuses on the downlink transmission, thus, it is not assured to be practical for uplink transmission.
The user grouping in V-MIMO has been implemented for the case where a large number of users are presented as shown by Karimi et al. (2013). The user grouping is performed in two computationally light steps to achieve high throughput. The first step ensures proportional fairness by assigning time slots to group the users. In the second step, an uplink grouping is selected from the large group of users by instantaneous SNR, while a round robin method selects the uplink groupings from smaller groups of users. The study aims to ensure that the transmission efficiency and throughput are higher when larger numbers of users are present.
Multiuser pairing using a sub-optimal algorithm in V-MIMO systems has been presented by Hongzhi et al. (2014). A sub-optimal multiuser pairing algorithm (SMPA), with low computational complexity, is proposed for selecting pairs of users. The objective of the paper is to maximize throughput through the proposed SMPA. The scheme is further simplified to ensure that the proposed algorithms could be performed with low complexity. Hence, a simplified sub-optimal algorithm for multiuser pairing with proportional fairness is proposed namely SS-PFMPA.
The Hungarian algorithm is shown in Abrar et al. (2014), Li et al. (2012), Chen et al. (2011) and Binary Switching Algorithm as shown in Zeger & Gersho (1990) have been implemented in Ruder et al. (2013). The paper focuses on the optimisation of user pairing and frequency allocation when V-MIMO of the SC-FDMA model is used. The combination of the Hungarian and Binary Switching algorithms performs both the user pairings and frequency allocation. The two algorithm approach is based on a optimal effort which has high computational complexity. These algorithms improve the bit error rate performance compared to user pairing and frequency allocation made by random selection. This scheme performs well when the number of users per group is two. Unfortunately, the computational complexity tends to increase when the number of users per group increases, and this problem has not been addressed in this paper. This paper has solved an extreme computational complexity which is provided by full search through implementing a sub-optimal algorithm by combining the Hungarian algorithm with Binary Switching algorithm. Two objective functions have been introduced, which are maximizing the capacity and minimizing the error rate. At first, the Hungarian algorithm is applied to allocate the best frequency carriers. Then, Binary Switching algorithm is applied to perform a single user swap in order to switch every user between groups. Even though this sub-optimal algorithm has solved the frequency allocation and user grouping method with low computational complexity, it is still considerably high especially when more users are incorporated in one group. This algorithm is suitable if the number of users per group is two but it becomes impractical when the number of users per group is more. The probability in performing the frequency allocation and user grouping will be extremely high while many other possibilities remain untested. The optimal number of user per group is not taken into account in their study and no concrete reason why two users per group is chosen. This paper does not also discuss more number of users per group and its results is based on two users per group. The simulation comparison is only made between their sub-optimal and optimal algorithm, hence, currently there is no other sub-optimal method which is proposed with low computational complexity.