Multiuser MIMO Systems Research Articles

Multi-User Hybrid MIMO at 60 GHz Using 16-Antenna Transmitters

Given the high throughput requirement for the next generation wireless communication systems, merging millimeter wave technologies and multi-user MIMO seems a very promising strategy to achieve the required 20 Gbps. Although a full digital architecture provides the best performance and flexibility, its implementation at millimeter-wave frequencies today seems unrealistic due to the prohibitive costs and high power consumption. Hybrid analog-digital architectures, efficiently sharing beamforming operations between analog and digital domains, appear as a feasible way to implement multi-user MIMO systems at millimeter wave frequencies. While hybrid architectures have been studied intensively, an effective and flexible demonstration proving the feasibility is still missing. In this paper, we introduce the design of a millimeter wave multi-user MIMO system with hybrid analog and digital processing using 60 GHz radios equipped with phased arrays. A base station, employing two 16-antennas transmitters, serves simultaneously two user equipment devices, each with 4 antenna elements, effectively realizing spatial multiplexing. We propose a complete system design comprising the description of millimeter radio transceivers, the multi-user hybrid MIMO algorithms including a strategy for channel estimation and frequency selective precoding along with the transmission protocol. We show that the spatial multiplexing is achieved in several scenarios, most importantly in a strong interference-limited scenario.

Beam Training and Allocation for Multiuser Millimeter Wave Massive MIMO Systems

We investigate beam training and allocation for multiuser millimeter wave massive MIMO systems. An orthogonal pilot based beam training scheme is first developed to reduce the number of training times, where all users can simultaneously perform the beam training with the base station (BS). As the number of users increases, the same beam from the BS may point to different users, leading to beam conflict and multiuser interference. Therefore, a quality-of-service (QoS) constrained (QC) beam allocation scheme is proposed to maximize the equivalent channel gain of the QoS-satisfied users, under the premise that the number of the QoS-satisfied users without beam conflict is maximized. To reduce the overhead of beam training, two partial beam training schemes, an interlaced scanning (IS) and a selection probability (SP) based schemes, are proposed. The overhead of beam training for the IS-based scheme can be reduced by nearly half while the overhead for the SP-based scheme is flexible. Simulation results show that the QC-based beam allocation scheme can effectively mitigate the interference caused by the beam conflict and significantly improve the spectral efficiency while the IS-based and SP-based schemes significantly reduce the overhead of beam training at the cost of sacrificing spectral efficiency a little.

Asymptotic Analysis of RZF in Large-Scale MU-MIMO Systems Over Rician Channels

In this paper, we focus on the downlink ergodic sum rate of a single-cell large-scale multiuser MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments (TIEs). A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each TIE uses a specific power and each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming that N and K grow large with a given ratio and perfect channel state information is available at the base station. New results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of system parameters, spatial correlation matrix, and Rician factor. Numerical results are used to validate the accuracy of asymptotic approximations in the finite system regime and to evaluate the performance under different operating conditions. It turns out that the asymptotic expressions provide accurate approximations even for relatively small values of N and K.

Joint Spatial Division and Diversity for Massive MIMO Systems

We propose a downlink beamforming scheme that combines spatial division and orthogonal space-time block coding (OSTBC) in multi-user massive MIMO systems. The beamformer is divided into two parts: a pre-beamforming matrix to separate the users into different beams with no interference between each other, which is designed based on the low rank covariance matrix of the downlink channel, and a linear precoding matrix using partial or even no channel state information (CSI) concatenated by an OSTBC. To construct the pre-beamforming matrix, a simple method that selects columns from DFT matrix is presented. To design the linear precoding matrix with partial CSI of the effective channel after the pre-beamforming, we solve an optimization problem to minimize the pairwise error probability (PEP) of the users under an individual power or sum power constraint, respectively. For the individual power constraint, a semi-definite relaxing (SDR) method with a sufficient condition achieving the globally optimal solution is proposed to provide a performance benchmark. In addition, an efficient iterative successive convex approximation (SCA) method is provided to achieve a suboptimal solution. Furthermore, closed form solutions are derived under some special cases. For the sum power constraint, we consider two different designs, i.e., minimizing the average PEP and minimizing the maximum PEP of all users. We find that both non-convex problems have a similar structure, and proposed a unified SCA-Alternating Direction Method of Multipliers (ADMM) algorithm to handle them. The SCA-ADMM method can be implemented in a parallel manner, and thus is with great efficiency. Simulation results show the efficiency of our proposed JSDD scheme and the optimization method.

Revisiting the MIMO Capacity With Per-Antenna Power Constraint: Fixed-Point Iteration and Alternating Optimization

In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are 1) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems and/or 2) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared with current methods. We then extend the proposed solutions to multiuser MIMO systems with dirty paper coding-based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches.

A Game Theoretic Approach to Uplink Pilot and Data Power Control in Multi-Cell Multi-User MIMO Systems

In multi-user multiple-input–multiple-output (MU-MIMO) systems that employ pilot-symbol aided channel estimation, the pilot-to-data power ratio (PDPR) has a large impact on the system performance. In this paper, we consider the problem of setting the PDPR in multi-cell MU-MIMO systems in the presence of channel estimation errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a model of the multi-cell MU-MIMO system and derive a closed-form expression for the mean squared error of the uplink received data symbols. Building on this result, we then propose two decentralized PDPR-setting algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that do not properly set the PDPR, while they maintain different levels of fairness.

Fair-CDUS for Equalization of User Selection Frequency in Massive Multi-User MIMO System

Multi-user technique is a method which allows a transmitter serves multiple users in the same time. In the Massive-MIMO, Multi-user technique could be implemented with the help of beamforming technique which allows the system to create several beams in the same time, where every beam serves a different user. In this paper, we introduce a method called Fair-Chordal Distance User Selection (Fair-CDUS) to equalize the user selection in Massive-MIMO System. The fairness level which is measured in this research is the frequency of user selection. In this paper, we also explain how to adjust the degree of the fairness with our proposed method.

Performance improvement of multi-user MIMO system in the presence of nonlinear distortion

In the presence of nonlinear distortion, the performance degradation of multiuser multiple-input multiple-output (MU-MIMO) systems becomes significant. The performance loss comes not only from the in-band nonlinear distortion, but also from the imperfect cancellation of multiuser interference (MUI) due to the nonlinear distortion. In this regard, we analyze characteristics of nonlinear distortion in an MU-MIMO system. Numerical results show that even tiny nonlinear distortion severely degrades the performance of MU-MIMO system. A nonlinear minimum mean square error (NMMSE) detection algorithm is proposed to improve the system performance. The proposed detection algorithm improves the system performance at low additional cost.

Notice of Violation of IEEE Publication Principles: A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems

Notice of Violation of IEEE Publication Principles A Robust Signal Detection Scheme for 5G Massive Multiuser MIMO Systems by Imran Khan in IEEE Transactions on Vehicular Technology, July 2018, 9597-9604 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copied portions of text from the following paper with no credit to the original work (including appropriate references to the original author(s) and/or paper title) and without permission. Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems by Lei Liu, Chau Yuen, Yong Liang Guan, Ying Li, and Yuping Su in IEEE Transactions on Wireless Communications, June 2016, pp.6487-6501 Aiming at the problem of high detection complexity at the base station in massive multiuser multiple-input multiple-output system, this paper proposes a low complexity variable node full information Gaussian message passing iterative detection based on forced convergence (VFI-GMPID-FC) algorithm. First, the traditional GMPID algorithm is improved to get the VFI-GMPID algorithm. The detection performance of the VFI-GMPID algorithm approaches the minimum mean square error (MMSE) detection algorithm, but the complexity is much lower than that of the MMSE algorithm. Combined with the idea of forced convergence and VFI-GMPID, the VFI-GMPID-FC algorithm is proposed to further reduce the complexity of the algorithm and improve the detection efficiency. Finally, the simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm while ensuring improved detection performance.

A Simple and Effective Approach for Transmit Antenna Selection in Multiuser Massive MIMO Leveraging Submodularity

Massive MIMO systems are expected to enable great improvements in spectral and energy efficiency. Realizing these benefits in practice, however, is hindered by the cost and complexity of implementing large-scale antenna systems. A potential solution is to use transmit antenna selection for reducing the number of radio-frequency (RF) chains at the base station. In this paper, we consider the NP-hard discrete optimization problem of performing transmit antenna selection in the downlink of a single cell, multiuser massive MIMO system by maximizing the downlink sum-rate capacity with fixed user power allocation subject to various RF switching constraints. Whereas prior work has focused on using convex relaxation based schemes, which lack theoretical performance guarantees and can be computationally demanding, we adopt a very different approach. We establish that the objective function of this antenna selection problem is monotone and satisfies an important property known as submodularity , while the RF switching constraints are expressible as the independent sets of a matroid. This implies that a simple greedy algorithm can be used to guarantee a constant-factor approximation for all problem instances. Simulations indicate that greedy selection yields a near-optimal solution in practice and captures a significant fraction of the total downlink channel capacity at substantially lower complexity relative to convex relaxation based approaches, even with very few RF chains. This paves the way for substantial reduction in hardware complexity of massive MIMO systems while using very simple algorithms.

Joint interference alignment and power allocation for NOMA-based multi-user MIMO systems

Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multi-user MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.

Wireless Powered Information Transfer Based on Zero-Forcing for Multiuser MIMO Systems

A zero-forcing (ZF)-based transmission method is proposed for a wireless powered communication network (WPCN) with multiuser multi-input multi-output (MU-MIMO) channels. We formulate the max–min user rate optimization problem based on ZF-based uplink transmission, under the transmit power constraints determined by the downlink power transfer, and derive a Newton-like iterative algorithm to find the optimal solution of the proposed problem. Using the proposed ZF-based method, we design a downlink energy beamformer, and optimize the uplink power allocation vector, precoder, and combiner in terms of the max–min user rate for a MU-MIMO WPCN system. Numerical simulations show that the proposed method provides at least 3 dB signal-to-noise ratio gain compared to the conventional transmission schemes for WPCN. Moreover, through complexity analysis and simulations, it is shown that the proposed method can reduce at least 92% of the computation load in terms of runtime with moderate achievable rate loss, compared to the optimal method obtained by the interior-point method.

Effect of User Antenna Selection on Block Beamforming Algorithms for Suppressing Inter-User Interference in Multiuser MIMO System

Effect of User Antenna Selection on Block Beamforming Algorithms for Suppressing Inter-User Interference in Multiuser MIMO System

Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems

Future cellular systems based on the use of millimeter waves will heavily rely on the use of antenna arrays both at the transmitter and at the receiver. For complexity reasons and energy consumption issues, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated. This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for a cellular multiuser MIMO (MU-MIMO) system with large number of antennas. Analytical formulas for the asymptotic achievable spectral efficiency and for the global energy efficiency of several beamforming structures are derived in the large number of antennas regime. Using the most recently available data for the energy consumption of phase shifters and switches, we show that fully-digital beamformers may actually achieve a larger energy efficiency than lower-complexity solutions, as well as that low-complexity beam-steering purely analog beamforming may in some cases represent a good performance-complexity trade-off solution.

Limited Feedback in Single and Multi-User MIMO Systems With Finite-Bit ADCs

Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit channel state information at the transmitter and receiver. This paper presents codebook designs and performance analyses for limited feedback MIMO systems with finite-bit ADCs. A point-to-point single-user channel is firstly considered. When the received signal is sliced by 1-bit ADCs, the absolute phase at the receiver is important to align the phase of the received signals. A new codebook design for beamforming, which separately quantizes the channel direction and the residual phase, is therefore proposed. For the multi-bit case where the optimal transmission method is unknown, suboptimal Gaussian signaling and eigenvector beamforming is assumed to obtain a lower bound of the achievable rate. It is found that to limit the rate loss, more feedback bits are needed in the medium SNR regime than the low and high SNR regimes, which is quite different from the conventional infinite-bit ADC case. Second, a multi-user system where a multiple-antenna transmitter sends signals to multiple single-antenna receivers with finite-bit ADCs is considered. Based on the derived performance loss due to finite-bit ADCs and finite-bit CSI feedback, the number of bits per feedback should increase linearly with the ADC resolution in order to restrict the rate loss.

다중사용자 다중입출력 시스템에서의 부분적으로 연결된 구조기반 하이브리드 빔포밍 기법

본 논문에서는 다중사용자 다중입출력 시스템에서의 하이브리드 빔포밍 기법을 소개한다. 기존에 제안된 Fully Connected Structure 기반의 하이브리드 빔포밍 기법은 하드웨어 복잡도가 크므로, 본 논문에서는 Partially Connected Structure 기반의 하이브리드 빔포밍 기법을 제안한다. 실험 결과를 통해 제안하는 기법의 성능을 확인한다.

A Beam Selection Algorithm for Millimeter-Wave Multi-User MIMO Systems

Millimeter wave (mmWave) frequencies offer huge transmission bandwidths and allow packing a large number of antennas within a given aperture area, enabling high-dimensional MIMO communications. A major bottleneck in realizing such systems is the requirement of a large number of RF chains. Working in the beamspace domain provides an attractive solution through beam selection . We propose a novel beam selection algorithm for downlink mmWave multi-user MIMO systems that selects $K$ beams for $K$ users. The proposed method attempts to maximize the sum-rate and nulls-out the multi-user interference. We show, through simulations, that the proposed method outperforms the existing ones.

전이중 다중 사용자 거대 다중안테나 시스템에서 기지국의 자기 간섭 수확을 위한 안테나 분할 및 선택 기법

본 논문에서는 전이중 거대 다중안테나 다중사용자 시스템에서 기지국이 자기 간섭을 수확함으로써, 시스템 에너지 효율을 높이는 연구에 대해 소개한다. 이를 위해, 기지국은 수신 안테나를 상향링크 데이터를 받기 위한 그룹과 자기 간섭을 수확하기 위한 그룹으로 나누어서 운용한다. 기지국은 통계적 채널 정보를 바탕으로 에너지 효율을 최대화하기 위한 각 그룹의 안테나 수를 설정하고, 이를 바탕으로 안테나 선택 알고리즘을 통해, 각 그룹에 해당하는 안테나를 선택하는 기법을 제안한다. 그리고 실험 결과를 통해 제안 시스템이 기존의 반이중 통신 시스템보다 높은 에너지 효율을 가짐을 검증한다.

On Detection and Mitigation of Reused Pilots in Massive MIMO Systems

In a time-division duplex multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multi-cell multi-user massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing semi-blind approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared with the in-cell users. This requires (perfect) power control. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. Finally, the estimated channels are used to detect information symbols, which, in turn, are used as pseudo-pilots to re-estimate the in-cell users’ channels. The proposed approach is illustrated via simulation examples and compared with some existing semi-blind methods.

Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm

Sparse code multiple access (SCMA), which combines the advantages of low density signature (LDS) and code-division multiple access (CDMA), is regarded as one of the promising modulation technique candidate for the next generation of wireless systems. Conventionally, the message passing algorithm (MPA) is used for data detector at the receiver side. However, the MPA-SCMA cannot be implemented in the next generation wireless systems, because of its unacceptable complexity cost. Specifically, the complexity of MPA-SCMA grows exponentially with the number of antennas. Considering the use of high dimensional systems in the next generation of wireless systems, such as massive multi-user MIMO systems, the conventional MPA-SCMA is prohibitive. In this paper, we propose a low complexity detector algorithm named the expectation propagation algorithm (EPA) for SCMA. Mainly, the EPA-SCMA solves the complexity problem of MPA-SCMA and enables the implementation of SCMA in massive MU-MIMO systems. For instance, the EPA-SCMA also enables the implemantation of SCMA in the next generation wireless systems. We further show that the EPA can achieve the optimal detection performance as the numbers of transmit and receive antennas grow. We also demonstrate that a rotation design in SCMA codebook is unnecessary, which is quite rather different from the general assumption