Multiuser MIMO Systems Research Articles

Transceiver Design for Simultaneous Wireless Information and Power Multicast in Multi-User mmWave MIMO System

Millimetre wave (mmWave) has a range of merits for both wireless information and power transfer, such as high-bandwidth for high-throughput, massive antennas for efficient signal transmission, highly directional beams for overcoming channel attenuations and high degree of freedom for signal processing. Therefore, we investigate transceiver design in a multi-user mmWave MIMO system for simultaneous wireless information and power multicast (SWIPM). First of all, we obtain the asymptotically optimal solution include full-digital solutions for the transmitter and information users (IUs) as well as analog solutions for energy users (EUs) in the mmWave MIMO aided transceivers. It maximizes information multicast spectral efficiency of IUs, while satisfying individual wireless charging requirement of EUs. Then, based on this asymptotically optimal solution, hybrid beamformer at a transmitter and signal combiners at IUs are jointly designed with the same objective, when considering low hardware complexity (e.g. limited number of RF chains and low-resolution phase shifters). A low-complexity algorithm is developed for solving the resultant constraint max-min problem. Additionally, we also demonstrate a practical multi-antenna aided receiver architecture of EUs by considering practical non-linear energy harvesters. Numerical results validate the advantage of our joint transceiver design for SWIPM.

Performance Analysis and Joint Statistical Beamformer Design for Multi-User MIMO Systems

In this letter, we propose techniques for joint transmit and receive beamforming in downlink multi-user MIMO systems under Rayleigh fading channels using knowledge of the second-order statistics. The contribution of our work is twofold. First, we derive a closed-form expression for the outage probability for a general channel model using a recent covariance shaping method and for the conventional Kronecker structured model. Second, we present an outage minimization technique obtained by designing the transmit and receive beamformers. The transmit beamformers maximize the statistics of the signal-to-leakage-plus-noise ratio, while the receive beamformers minimize the cross-covariance of all users. Our techniques are “blind” in the sense that they do not require the transmission of pilot symbols, nor the estimation of the instantaneous state of the channel, making them bandwidth efficient. The derived theoretical results are validated via Monte Carlo simulations and show significant reduction in the outage probability by employing the proposed statistical beamforming techniques.

빔공간 다중 사용자 MIMO 시스템에서 간섭제거를 위한 빔 선택 기법

본 논문에서는 빔공간 다중 사용자 MIMO (Multiple-Input Multiple-Output) 시스템에서 간섭 제거를 위한 빔 선택 기법을 제안한다. 제안하는 기법은 오프라인 핑거프린트 데이터베이스 구성 및 온라인 간섭제거를 위한 빔 선택 두 단계로 구성된다. 오프라인 단계에서는 최상의 빔을 기준으로 사용자를 그룹화 한다. 사용자 그룹화 후 각 그룹에 대한 핑거프린트 데이터베이스를 구축한다. 온라인 단계에서는 그룹 기반 핑거프린트 데이터베이스 정보를 사용하여 다중 사용자 간 빔 간섭을 제거하기 위해 서로 다른 빔을 선택한다. 시뮬레이션 결과 제안하는 빔 선택 기법이 훨씬 높은 에너지 효율로 전디지털 시스템에 근접한 합률(sum-rate) 성능을 갖는다.

Low complexity based scheduling methods for multi-user MIMO systems

The process of joint user and receive antenna scheduling is very vital in the uplink direction of multi-antenna wireless data communications system with multiple users. With higher number of receive antennas and active users, the search space becomes huge for the joint user and receive antenna scheduling process. It is very difficult to accomplish the searching through the entire search space within the wireless communication time frame. In this paper, low complexity based methods for joint scheduling of users and receive antennas have been proposed. Here, this has been shown that low complexity based scheduling scheme is able to attain system throughput which is quiet close to that of exhaustive search algorithm (by searching the entire search space). Moreover, an interesting observation can be made that the number of complex multiplications and additions required by these low complexity methods is quite less than that of exhaustive search algorithm.

Efficient Techniques for In-Band System Information Broadcast in Multi-Cell Massive MIMO

We consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We consider two different types of SI broadcast, i) synchronous broadcast of common (i.e., same) SI symbols from all cells, and ii) synchronous broadcast of cell-specific SI symbols from each cell. Through analysis we derive expressions for the achievable sum rate to STs in each cell and the rate of SI transmission to an IT for both these types of SI broadcast. We also derive expressions for the sum rate to STs and the rate to an IT for traditional orthogonal access (OA) where a fraction of physical resource is reserved for broadcast of SI. Simulations reveal that, just as in the single-cell scenario, for the multi-cell scenario also, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

Performance Analysis of Intelligent Reflecting Surface in Multi-user MIMO Systems

Intelligent Reflecting Surface (IRS) is a revolutionary technology which is able to improve the performance of wireless data transmission system. Specially, large numbers of small reflecting unit are jointly adjusted to reconfigure the wireless signal transmitting environment. In this paper, we study the use of IRS in a multi-user MIMO system where IRS is deployed to assist the data transmission in both uplink and downlink phases. We first present an overview of the technology of intelligent surfaces, including its fundamental principles and practical applications. Then we derive the signal model of IRS aided multi-user MIMO system and provide data transmission schemes. Finally, numerical results are demonstrated to show the superb signal enhancement with the use of intelligent surfaces in wireless system.

High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO With Processing-in-Memory

All-digital basestation (BS) architectures enable superior spectral efficiency compared to hybrid solutions in massive multi-user MIMO systems. However, supporting large bandwidths with all-digital architectures at mmWave frequencies is challenging as traditional baseband processing would result in excessively high power consumption and large silicon area. The recently-proposed concept of finite-alphabet equalization is able to address both of these issues by using equalization matrices that contain low-resolution entries to lower the power and complexity of high-throughput matrix-vector products in hardware. In this brief, we explore two different finite-alphabet equalization hardware implementations that tightly integrate the memory and processing elements: (i) a parallel array of multiply-accumulate (MAC) units and (ii) a bit-serial processing-in-memory (PIM) architecture. Our all-digital VLSI implementation results in 28nm CMOS show that the bit-serial PIM architecture reduces the area and power consumption up to a factor of $ 2\times $ and $3\times $ , respectively, when compared to a parallel MAC array that operates at the same throughput.

On the MMSE‐based multiuser millimeter wave MIMO hybrid precoding design

SummaryA hybrid MMSE‐based precoder and combiner design with low complexity is studied in this paper for both the downlink and uplink multiuser millimeter wave MIMO systems. At first, according to the underlying channel knowledge, the phases of the RF precoder and combiner are manipulated by using the MMSE criterion. On the basis of the MMSE‐based analog precoder design, the MMSE‐based digital baseband precoder can be readily derived. Thus, this method combines the advantage of using the MMSE precoding and phase extraction technique to obtain a reasonable solution. The proposed hybrid precoding design will be examined in both the uplink and downlink multiuser MIMO systems. Numerical results are presented to show that the proposed MMSE‐based hybrid precoding design is capable of attaining superior performance in terms of both the spectral efficiency and energy efficiency over the existing designs. Moreover, we will show that the MMSE‐based design can be readily extended to the multiuser millimeter wave MISO systems when only statistical channel state information is available.

Optimal power allocation with limited feedback of channel state information in multi-user MIMO systems

In this paper, an expression for the user's achievable data rate in the multi-user multiple-input multiple-output (MU-MIMO) system with limited feedback (LF) of channel state information (CSI) is derived. The energy efficiency (EE) is optimized through power allocation under quality of service (QoS) constraints. Based on mathematical equivalence and Lagrange multiplier approach, an energy-efficient unequal power allocation (EEUPA) with LF of CSI scheme is proposed. The simulation results show that as the number of transmitting antennas increases, the EE also increases which is promising for the next generation wireless communication networks. Moreover, it can be seen that the QoS requirement has an effect on the EE of the system. Ultimately, the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation (EEEPA) with LF of CSI schemes.

Efficient Low-Complexity Antenna Selection Algorithms in Multi-User Massive MIMO Systems With Matched Filter Precoding

In this work, two efficient low complexity Antenna Selection (AS) algorithms are proposed for downlink Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) systems with Matched Filter (MF) precoding. Both algorithms avoid vector multiplications during the iterative selection procedure to reduce complexity. Considering a system with N antennas at the Base Station (BS) serving K single-antenna users in the same time-frequency resources, the first algorithm divides the available antennas into K groups, with the kth group containing the N/K antennas that have the maximum channel norms for the kth user. Therefore, the Signal-to-Interference plus Noise Ratio (SINR) for the kth user can be maximized by selecting a subset of the antennas from only the kth group, thereby resulting in a search space reduction by a factor of K. The second algorithm is a semiblind interference rejection method that relies only on the signs of the interference terms, and at each iteration the antenna that rejects the maximum number of interference terms will be selected. The performance of our proposed methods is evaluated under perfect and imperfect Channel State Information (CSI) and compared with other low complexity AS schemes in terms of the achievable sum rate as well as the energy efficiency. In particular, when the Signal-to-Noise Ratio (SNR) is 10 dB, and for a system with 20 MHz of bandwidth, the proposed methods outperform the case where all the antennas are employed by 108.8 and 49.2 Mbps for the first and second proposed algorithms, respectively, given that the BS has perfect CSI knowledge and is equipped with 256 antennas, out of which 64 are selected to serve 8 single-antenna users.

Multi-user massive MIMO channel estimation using joint sparsity and non-ideal feedback modeling

In order to realize the advantages of the massive MIMO systems, the channel state information must be obtained at the base station (BS). However, it is a challenging task in the frequency division duplexing (FDD) systems due to the overwhelming overhead of downlink channel pilots and uplink channel feedback. In this paper, we consider the multi-user massive MIMO system and apply joint sparsity-based technique to reduce the overhead. Due to the shared scatterers in the physical propagation environment, the user channel matrices have a joint sparsity structure which has been utilized in the proposed algorithm. Furthermore, using the common sparsity pattern of the uplink and downlink channel, we have suggested a mixed weighted smoothed L0/L2 norm minimization technique to estimate the downlink channel with the aid of the uplink channel support. Moreover, in most of the works in the literature, the feedback channel has been considered as a simple additive white Gaussian noise (AWGN) channel which may result in poor performance in the real scenarios. To address this issue, we have presented a better modeling of the uplink feedback channel to increase the channel estimation accuracy and decrease the number of required pilot sequences. We have investigated the performance of the proposed method in different simulation scenarios. The results confirm that the suggested scheme is superior to the other state-of-the-art techniques in terms of the recovery accuracy and the pilot and feedback overhead.

An Unfolded Pipelined Polar Decoder With Hybrid Number Representations for Multi-User MIMO Systems

Next generation wireless communication introduces ultra high speed and low latency requirements to baseband processors, especially for multi-user multiple-input-multiple-output (MU-MIMO) systems. Polar codes, as the channel coding scheme in the control plane of 5G eMBB scenario, still cannot satisfy the hardware implementation requirements of polar decoders for MU-MIMO systems. In this brief, a high throughput and low latency polar decoder is proposed for MU-MIMO systems. The decoding architecture is fully unfolded to maximize hardware parallelism, and is fully pipelined by decoupling the round-trip scheduling datapath to enable simultaneous processing of multiple codewords. Besides, a hybrid decoding datapath is introduced by proposing complementary decoding units with mixed data representations to reduce hardware complexity. For (1024, 512) polar codes, a decoding throughput of 100.6 Gbps is achieved, and the latency is 0.43 us when 36 users are processed simultaneously.

A Symmetric Successive Overrelaxation (SSOR) based Gauss-Seidel Massive MIMO Detection Algorithm

With an increasing number of antennas and users, the complexity increases dramatically, in order to simplify the process. In this paper, We propose existed algorithms discussing their principles and their advantages as well as the comparison between each method and then we provide the possible improvement in theory changing steps in iteration process to avoid the matrix inversion, by introducing a pre-conditioned gradient (PCG) method and further improved Jacobi method and using Neumann-series terms to estimate an approximate matrix inversion and the Gauss-Seidel (GS) method with a diagonal-approximate initial solution to the method and biconjugate gradient stabilized (BICGSTAB) method and above all of the methods are based on the minimum mean square error (MMSE) detector including the application in multi-user MIMO system. Another approach is from a signal detector based on symmetric successive over relaxation (SSOR) method without matrix inversion and Neumann series based on the zero forcing signal detector. The third algorithm uses a modified version of the conjugate gradient least square (CGLS). These methods are mainly replacing the inversion process into iteration process to reduce the complexity of the algorithm not only replacing the stages but optimizing the speed from setting the initial solution as well as the improvement using the Neumann-series to replace the iteration part. Finally, we proposed a symmetric successive overrelaxation (SSOR) based Gauss-Seidel for massive MIMO detection.

Constrained Channel Decomposition-Based Hybrid Beamforming for mmWave Massive MIMO Systems

In this article, we propose a hybrid beamforming design for multiuser mmWave massive MIMO systems. We adopt a two-stage approach for designing the analog and digital beamforming separately. The analog beamforming design is based on a constrained low-rank channel decomposition and aims to simultaneously harvest the array gain and reduce the intra- and inter-user interference. The digital beamforming design is conducted by using the regularized channel diagonalization method, which provides a better trade-off between multiuser interference suppression and transmit diversity, thus attaining a better performance in low-SNR scenarios or when communicating to many users or through many data streams. We validate the effectiveness of the proposed design through numerical simulations, which have shown that our design outperforms several other hybrid beamforming designs in the literature.

Digital Predistortion for Multiuser Hybrid MIMO at mmWaves

Efficient mitigation of power amplifier (PA) nonlinear distortion in multi-user hybrid precoding based broadband mmWave systems is an open research problem. In this article, we first carry out detailed signal and distortion modeling in broadband multi-user hybrid MIMO systems, with a bank of nonlinear PAs in each subarray, while also take the inevitable crosstalk between the antenna/PA branches into account. Building on the derived models, we then propose an efficient closed-loop (CL) digital predistortion (DPD) solution that requires only a single-input DPD unit per transmit chain or subarray, despite crosstalk, providing substantial complexity-benefit compared to the state-of-the art multi-dimensional DPD solutions. We show that under spatially correlated multipath propagation, each DPD unit can provide linearization towards every intended user, or more generally, towards all spatial directions where coherent propagation is taking place, and that the proposed CL DPD system is robust against crosstalk}. Extensive numerical results building on practical measurement-based mmWave PA models are provided, demonstrating and verifying the excellent linearization performance of the proposed DPD system in different evaluation scenarios.

Quantum Bacterial Foraging Optimization: From Theory to MIMO System Designs

This article develops a quantum bacterial foraging optimization (QBFO) algorithm, a quantum intelligence algorithm based on quantum computing and bacterial foraging optimization (BFO), with application in MIMO system optimization designs. In QBFO, a multiqubit is used to represent a bacterium, and a quantum rotation gate is used to mimic chemotaxis. Because the quantum bacterium with multiqubit has the advantage that it can represent a linear superposition of states (binary solutions) in search space probabilistically, the proposed QBFO algorithms shows better performance on solving combinatorial optimization problems than its classical counterpart BFO and Quantum Genetic Algorithm (QGA), especially for parallel non-gradient optimization. A sparse channel estimation scheme based on QBFO with adaptive phase rotation (AQBFO) in 3D MIMO system is proposed, and simulation results show that AQBFO achieved a better performance than existing algorithms including least squares (LS), iteratively reweighted least squares (IRLS), matching pursuit (MP), and orthogonal matching pursuit (OMP). We further improve some critical aspects such as reproduction and dispersal processes of AQBFO, propose an improved IQBFO algorithm, and apply it for interference coordination in 3D multi-cell multi-user MIMO systems, aiming to maximize the spectral efficiency. It considers user fairness and jointly optimizes cell-center and cell-edge user specific antenna downtilts and power to maximize each user's sum rate. This problem is a combinatorial non-convex optimization problem that cannot be solved by the traditional Karush-Kuhn-Tucker Lagrangian algorithm whereas the IQBFO algorithm solves it effectively.

Design of Massive Multiuser MIMO System to Mitigate Inter Antenna Interference and Multiuser Interference in 5G Wireless Networks

Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput and spectral efficiency in 5G communication networks. Inter-antenna Interference (IAI) and Multi-user Interference (MUI) are two major factors that influence the performance of MU–MIMO system. IAI arises due to closely spaced multiple antennas at each User Terminal (UT), whereas MUI is generated when one UT comes in the vicinity of another UT of the same cellular network. IAI can be mitigated by the use of a pre-coding scheme such as Singular Value Decomposition (SVD) and MUI can be cancelled through efficient Multi-user Detection (MUD) schemes. The highly complex and optimal Maximum Likelihood (ML) detector involves a large number of computations, especially when in massive structures. Therefore, the local search-based algorithm such as Likelihood Ascent Search (LAS) has been found to be a better alternative for mitigation of MUI, as it results in near optimal performance using lesser number of matrix computations. Most of the literature have been aimed at mitigating either IAI or MUI, whereas the proposed work presents SVD pre-coding and LAS MUD to mitigate both IAI and MUI. Simulation results indicate that the proposed scheme can attain near-optimal bit error rate (BER) performance with fewer computations.

SDMA Grouping Based on Unsupervised Learning for Multi-User MIMO Systems

In this study, we investigate a spatial division multiple access (SDMA) grouping scheme to maximize the total data rate of a multi-user multiple input multiple output (MU-MIMO) system. Initially, we partition the set of mobile stations (MSs) into subsets according to their spatial compatibility. We explore different clustering algorithms, comparing them in terms of computational complexity and capability to partition MSs properly. Since we consider a scenario with a massive arrange of antenna elements and that operates on the mmWave scenario, we employ a hybrid beamforming scheme and analyze its behavior in terms of the total data rate. The analog and digital precoders exploit the channel information obtained from clustering and scheduling, respectively.  The simulation results indicate that a proper partition of MSs into clusters can take advantage of the spatial compatibility effectively and reduce the multi-user (MU) interference. The hierarchical clustering (HC) enhances the total data rate 25% compared with the baseline approach, while the density-based spatial clustering of applications with noise (DBSCAN) increases the total data rate 20%.

Design and Prototyping of Hybrid Analog–Digital Multiuser MIMO Beamforming for Nonorthogonal Signals

To enable user diversity and multiplexing gains, a fully digital precoding multiple-input-multiple-output (MIMO) architecture is typically applied. However, a large number of radio frequency (RF) chains make the system unrealistic to low-cost communications. Therefore, a practical three-stage hybrid analog-digital precoding architecture, occupying fewer RF chains, is proposed aiming for a nonorthogonal Internet of Things (IoT) signal in low-cost multiuser MIMO systems. The nonorthogonal waveform can flexibly save spectral resources for massive devices connections or improve data rate without consuming extra spectral resources. The hybrid precoding is divided into three stages, including analog domain, digital domain, and waveform domain. A codebook-based beam selection simplifies the analog-domain beamforming via phase-only tuning. Digital-domain precoding can fine-tune the codebook shaped beam and resolve multiuser interference in terms of both signal amplitude and phase. In the end, the waveform-domain precoding manages the self-created intercarrier interference (ICI) of the nonorthogonal signal. This article designs over-the-air signal transmission experiments for fully digital and hybrid precoding systems on software-defined radio (SDR) devices. Results reveal that waveform precoding accuracy can be enhanced by hybrid precoding. Compared to a transmitter with the same RF chain resources, hybrid precoding significantly outperforms fully digital precoding by up to 15.6 dB error vector magnitude (EVM) gain. A fully digital system with the same number of antennas clearly requires more RF chains and, therefore, is low power, space-efficient, and cost-efficient. Therefore, the proposed three-stage hybrid precoding is a quite suitable solution to nonorthogonal IoT applications.

Position-Aided Fast Beam Training in mm-Wave Multiuser MIMO Systems

A position-aided fast beam training in mm-Wave multiuser MIMO systems (PAFBT) method is proposed, in the case of inexact position information. Position information can be used to compute the pointing direction and essentially eliminate the beam alignment overhead. But the incorrect position information will introduce the beam misalignment. To solve the estimated received nodes location uncertainty problem, a beam training scheme is conducted by transmitting the beams to cover critical areas of the received nodes to find out the best transmitter and receiver beams for establishing the links in mm-Wave multiuser MIMO systems. Mathematical and simulation analysis confirm the superiority of the proposed method over the conventional ones in performance.