Multiuser Multiple-input And Multiple-output Systems Research Articles

User Selection in MU-MIMO System with Limited CSI Feedback

In this paper, the concept of multi-channel integrated SINR is proposed for Multi-User Multiple Input and Multiple Output (MU-MIMO) limited feedback system where all equivalent channels are used to transmit data. We prove that the achievable bit rate of user terminal depends on its multi-channel integrated SINR. We propose a user selection algorithm based on the multi-channel integrated SINR which can obtain larger system throughput compared with the existing literatures where only one equivalent channel is used to transmit data, and we prove that the proposed algorithm has a smaller computation load compared with algorithms which directly calculate user’s achievable bit rate to select users.

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

Square-Root Generalized Eigenvalue Decomposition Processor for Leakage-Based Multi-User MIMO Precoding With Multi-Antenna Users

Multi-user multiple-input and multiple-output (MU-MIMO) precoding is an effective transmission scheme for achieving very high spectral efficiency in modern wireless communication systems. Although numerous multi-user precoder designs have been proposed over the last few decades, the signal-to-leakage-and-noise ratio (SLNR)-based precoder is known to achieve favorable performance-complexity tradeoffs. This paper presents a new generalized eigenvalue decomposition (GEVD) processor, that is, the core processing unit dominating the overall complexity in SLNR-based precoders. A low-complexity square-root algorithm is adopted to eliminate the need for matrix multiplications and matrix inversion computations, which reduces the complexity of the GEVD processor considerably. Finally, the proposed processor was designed and implemented by using a 40-nm complementary metal–oxide–semiconductor technology, which exhibited a maximum throughput rate of 1.1-M matrices/s for the MU-MIMO system.

Joint Antenna Selection and User Scheduling for Massive Multiuser MIMO Systems With Low-Resolution ADCs

The use of low-resolution data converters is regarded as a cost-effective solution to reduce the hardware complexity and power consumption of massive multiuser multiple-input and multiple-output (MIMO) systems. In this study, we focus on the problem of joint antenna selection and user scheduling (JASUS) in massive multiuser MIMO uplink systems, in which the base station (BS) is equipped with low-resolution analog-to-digital converters. We aim to simultaneously obtain the optimal BS antenna and user sets that maximize the system sum rate. However, finding the optimal solution to the JASUS problem requires an exhaustive search of all possible combinations of BS antennas and users, and this search incurs a combinatorial complexity that scales exponentially with the number of BS antennas and users. We address this problem by proposing a novel algorithm developed from the cross-entropy optimization (CEO) framework. The simulations produce promising results for the proposed CEO-based JASUS algorithm, which achieves a higher sum rate and a lower symbol error rate compared with other test algorithms.

Alternating Minimization Algorithms for One-Bit Precoding in Massive Multiuser MIMO Systems

The use of low-cost and power-efficient hardware components has elicited considerable interest in the context of massive multiuser multiple-input and multiple-output (MIMO) systems because such hardware architecture enables significant power and cost savings at the base station (BS). In this study, we focus on the downlink precoding problem in massive multiuser MIMO systems, where each BS antenna element is equipped with 1-bit digital-to-analog converters (DACs). We aim to find the optimal precoding vector and the associated precoding factor, such that multiuser interference can be minimized for all users. However, such precoding design problem with 1-bit DACs at the BS is non-convex and challenging because of the coupled precoding vector and associated precoding factor. To deal with this problem, we propose a two-step iterative procedure that applies the alternating minimization (AltMin) framework to alternately solve for the precoding vector and the associated precoding factor while keeping the other constant. The primary difficulty is the precoding vector design, which we address by proposing a low-complexity algorithm developed from the coordinate descent framework to find the optimal precoding vector. Simulation results show that the proposed AltMin algorithm not only exhibits low complexity but can also achieve a bit error rate performance that is comparable with or higher than those of state-of-the-art algorithms under various test scenarios.

Manifold Optimization Approach for Data Detection in Massive Multiuser MIMO Systems

When the number of base station (BS) antennas is considerably larger than the number of user terminals (UTs), a simple linear minimum mean-square-error (LMMSE) data detection algorithm is near-optimal for uplink massive multiuser multiple-input and multiple-output (MIMO) systems. However, the LMMSE detector suffers significant performance loss when the number of UTs is comparable to the number of BS antennas. To alleviate the performance loss caused by the decrease in the BS-antenna-to-UT ratio, known data detection algorithm, namely “TASER,” can yield bit error rate (BER) performance close to that of the optimal detector in symmetric massive multiuser MIMO systems, where the BS-antenna-to-UT ratio is one. However, this TASER algorithm can only handle binary and quadratic phase-shift keying (PSK) modulations. Moreover, the computational complexity of the TASER algorithm is still remarkably high. Thus, a novel data detection algorithm is proposed in this study to reduce the computational complexity of the detection algorithm while achieving acceptable BER performance. The proposed algorithm is developed from the retraction-based Riemannian manifold optimization (RMO) framework for symmetric massive multiuser MIMO systems. Unlike the TASER-based detector, the proposed RMO-based detector can be applied for general $M$ -ary PSK modulations. Simulation results show that the proposed detector significantly outperforms the LMMSE detector in terms of BER performance. In addition, the required complexity of RMO is substantially lower than that of TASER. These findings indicate that the proposed RMO-based detector exhibits a highly desirable tradeoff between BER performance and complexity.

Average Mutual Information Analysis of Multiple Input and Multiple Output System with Multihop Relaying in Wireless Communication System

Problem statement: Multiple Input and Multiple Output (MIMO) system with Multihop relaying techhnique is significant and active areas of wireless communication. In a rich scattering environment MIMO antenna system provides better channel capacity and data rates than single antenna systems. To provide high throughput, reliable transmission and broad coverage, wireless relaying techniques are essential in a variety of applications. In a cellular environment a relay can be used to overcome shadowing effect due to obstacles and multihop relaying can improve the throughput for mobiles suffering from poor signal to interference, noise ratio at the edge of a cell and reduce cell size to increase spectral efficiency. Approach: This study analyzes average mutual information of the Ricean channel for single hop Multiple Input Multiple Output (MIMO) system for different antenna configuation and dependence of capacity on the Rice factor for cellular system. The asymptotic capacity of multiuser two-hop MIMO system with Regularization Block Diagonalization (RBD) precoding techniques for Indepenent Identically Distributed (IID) channal and realistic Mobile to Mobile fading channel model was examined. In this realistic model, Non-Line-Sight (NLOS) propagation conditions are assumed from source mobile station to mobile relay and also from mobile relay to destination station. A non regenerative Amplify and Forward (AF) relay is used to optimize the capacity between the source and destination and also the evaluation was made for multiuser multi-hop relay system with correlated fading channel model using RBD precoding matrix. Results: The simulation results for average mutual information of single hop MIMO relay system for different antenna configuration with ricean channel model, ergodic sum mutual information for two hop relay system with RBD precoding technique and mutual information results for multiuser multihop relay system with correlated channel model was presented. Conclusion/Recommendations: Cellular systems generally operated at a fairly low SINR which can be increased on each hop by adding relays. Multiuser two hop relay system with RBD precoding for realistic mobile to mobile fading channel model is simulated and compared with IID channel model. Multiuser Multi-hop relay with optimal precoding and RBD precoding technique results were analyzed for correlated fading channel and found that capacity offered by RBD precoding scheme is better than other relaying systems.

Cross-Layer Antenna Selection and Channel Allocation for MIMO Cognitive Radios

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Furthermore, using the user data rate results, a learning-based channel allocation algorithm is proposed. Finally, numerical results are presented for performance evaluation of the proposed antenna selection and channel allocation algorithms.