Multi-user Multiple-input And Multiple-output Research Articles

Multiuser beamforming optimization for IRS‐aided systems with discrete phase shifts

Here, the beamforming design for multiuser multiple-input and multiple-output (MIMO) downlink transmission aided by an intelligent reflecting surface (IRS) with discrete phase shifts is studied. The authors deduce a minimum signal-to-interference-plus-noise-ratio (SINR) maximization fairness problem with the transmit beamformer, the receive beamformer and IRS phase shifts as optimization variables, which is generally NP-hard. Hence, an alternating optimization algorithm based on the gradient extrapolated majorization-minimization (GEMM) approach is employed to solve the above problem. Specifically, when the transmit beamformer and reflective phase shift are fixed, the authors give an optimal closed expression for the receive beamformer. Then, the receive beamformer is fixed, and the minimum SINR maximization problem at the transmitter is transformed into a power minimization problem. Finally, under the zero-forcing (ZF) transmitter beamformer, the GEMM algorithm is used to optimize the reflective phase shift. Simulation results show that by comparing to existing methods, the proposed GEMM algorithm can almost achieve the same performance with lower complexity.

Dual-Band 6 × 6 MIMO Antenna System for Glasses Applications Compatible with Wi-Fi 6E and 7 Wireless Communication Standards

Multi-user multiple-input and multiple-output (MU-MIMO) systems are the mainstream of current antenna design. This paper proposes a dual-band 6 × 6 MIMO glasses antenna for Wi-Fi 6E and Wi-Fi 7 indoor wireless communication. The six antennas have the same structure, all of which are F-shaped monopole antennas. They are on the left and right temples, at the upper and lower ends of the left and right frames, which effectively uses the space of the glasses. The substrate uses FR4 (εr=4.4, tanδ=0.02). The antenna design is compact (9 mm × 50 mm × 0.8 mm) and the glasses model is made of FR4. The overall model is similar to virtual reality (VR) glasses, which are convenient for a user to wear. The proposed antenna has three working frequency bands, at 2.4 GHz, 5 GHz, and 6 GHz. Through matching and optimization, the reflection coefficient can be lower than −10 dB. In addition, this paper evaluates two usage environments for simulation and measurement on the head and free space. The measurement results show that when the operating frequency band is at 2.45 GHz, the antenna efficiency is 86.1%, and the antenna gain is 1.9 dB. At 5.5 GHz, the antenna efficiency is 86.5%, and the antenna gain is 4.4 dB. At 6.7 GHz, the antenna efficiency is 85.4%, and the antenna gain is 3.7 dB. When the isolation of the MIMO antenna system is optimized, the low-frequency band is better than −10 dB, and the high-frequency band is better than −20 dB. The measured envelope correlation coefficient (ECC) values are all lower than 0.1.

Joint Optimization of Transmission and Computation Resources for Satellite and High Altitude Platform Assisted Edge Computing

In this paper, we investigate a satellite-aerial integrated edge computing network (SAIECN) to combine a low-earth-orbit (LEO) satellite and aerial high altitude platforms (HAPs) to provide edge computing services for ground user equipment (GUE). In the SAIECN, GUE’s computing tasks can be offloaded to HAP(s) or LEO satellite. In this paper, we minimize the weighted sum energy consumption of SAIECN via joint GUE association, multi-user multiple input and multiple output (MU-MIMO) transmit precoding, computation task assignment, and resource allocation. To solve the nonconvex problem, we decompose the optimization problem into four subproblems and solve each one iteratively. For the GUE association subproblem, quadratic transform based fractional programming (QTFP) and difference of convex function are utilized. The MU-MIMO transmit precoding subproblem is solved via QTFP and the weighted minimum mean-squared method. The computation task assignment is addressed using the classic interior point method while the computation resource allocation is derived in closed form. The numerical results show that the proposed SAIECN and the corresponding algorithm can solve the satellite based edge computing quite well and the energy cost is maintained at a relative low level.

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems.

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

Motion-Aware Optimizations for Downlink MU-MIMO in 802.11ax Networks

Multi-User Multiple-Input and Multiple-Output (MU-MIMO) is a technique that allows concurrent transmissions between one access point (AP) and multiple clients to improve spectral efficiency. In practice, however, the MU-MIMO is sensitive to client mobility and is sometimes even harmful to the performance in networks with moving clients. In this paper, we identify that it is essential to optimize the MUMIMO performance with moving clients by jointly selecting the sounding period, the number of spatial streams, and client grouping with the consideration of the client density of the network. We develop a data-driven model that estimates client throughput with the consideration of these parameters, as well as an algorithm that jointly determines the parameters for each client with low computational complexity. Using a commodity 802.11ax network, we experimentally demonstrate the significant impact of the key factors on MU-MIMO performance. Based on experimental data, we develop an emulation model to evaluate network performance with different client densities and mobility. Emulation results show that our proposed algorithm outperforms conventional schemes by over 20% in MU-MIMO networks with moving clients.

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.

An Efficient Method for Combining Multi-User MIMO Tomlinson-Harashima Precoding With User Selection Based on Spatial Orthogonality

Combining multi-user multiple-input and multiple-output (MU-MIMO) Tomlinson-Harashima precoding (THP) with semiorthogonal user selection (SUS) yields both space diversity and multi-user diversity benefits; however, it entails a significant computational effort. To overcome this limitation while retaining system-level performance, this paper proposes an efficient method for combining MU-MIMO THP with SUS. The proposed method is inspired by the fact that the ordering calculations in both THP and SUS are generally based on spatial orthogonality. Particularly, the ordering result of SUS is fully reused for the ordering in THP, thereby eliminating the ordering calculation in the latter. Through a system-level performance evaluation, the proposed method is compared to a traditional method with the ordering process, thereby demonstrating the effectiveness of the proposed method. Numerical results obtained via computer simulations show that although the proposed method achieves almost the same system capacity as the traditional MU-MIMO THP method with ordering, the computational complexity of the proposed method decreases substantially with an increase in the number of antennas.

Joint MU-MIMO Precoding and Resource Allocation for Mobile-Edge Computing

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

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.

Sensing and throughput analysis of a MU-MIMO based cognitive radio scheme for the Internet of Things

State-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future Internet of Things (IoT) rollout. In this paper, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of Things (CR-IoT) with weighted-eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with MIMO antennas; we calculate the WEVD ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain and enhanced system throughput in comparison to the conventional scheme with eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment (i.e. SNR < -28). Furthermore, the proposed scheme has a lower energy consumption, prolonged expected lifetime and achieves a low error probability when compared with other schemes like the conventional single-input and single-output (SISO) based CR-IoT scheme with EVD and ED spectrum sensing.

Efficient Frame Construction for Multi-User Transmission in IEEE 802.11 WLANs

The latest standard of IEEE 802.11 WLANs embraces multi-user (MU) transmission via orthogonal frequency-division multiple access (OFDMA), multi-user multiple-input and multiple-output (MU-MIMO), or a mixture of both. However, due to the frame-basis transmission of WLANs, all concurrent user frames must have an identical transmission duration so as to fit in a common MU frame, which requires shorter frames padded with dummy bits, thus reducing transmission efficiency. Since stations are likely to have heterogeneous traffic demands and transmission bit rates in real networks, such inefficiency arises as a practical problem. To address this challenge, first we analyze how different overhead components impact the efficiency of MU transmission. Then, we propose a scheme to construct an MU frame with an optimal length maximizing its transmission efficiency, which provides a unified framework for OFDMA and MU-MIMO in both downlink and uplink. Through simulation, we demonstrate that the proposed scheme integrated with various scheduling algorithms reduces transmission delay and enhances traffic delivery ratio considerably compared to basic approaches.

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.

Interference coordination for millimeter wave communications in 5G networks for performance optimization

To address the increasing data rate demands for future wireless networks, a dense deployment of base stations or access points is the most promising approach; however, doing so may cause high intercell interference (ICI). Numerous interference coordination (IC) approaches have been proposed to reduce ICI. Conducting 5G communication on millimeter wave (mmWave) bands is more complex because of its higher propagation losses and greater attenuation variance, all of which depend on environment change. Massive antenna arrays with beamforming techniques can be used to overcome high propagation loss, reduce interference, deliver performance gains of coordination without a high overhead, and deliver high network capacity with multiplex transmitters. The central challenge of a massive antenna array that uses beamforming techniques is coordinating the users and beams for each transmitter within a large network. To address this challenge, we propose a novel two-level beamforming coordination approach that partitions a large network into clusters. At the intracluster level, this approach performs intracluster coordination similar to the user selection algorithms in a multiuser multiple input and multiple output (MU-MIMO); doing this maximizes the utility function or minimizes the signal-to-interference-plus-noise ratio (SINR) function within a cluster. A dynamic time domain IC approach is employed at the intercluster level, collecting interference information for cluster-edge user equipment (UE) and allocating the UE dynamically among the clusters to reduce the intercluster interference for a switched-beam system (SBS). Simulation results show that the proposed two-level IC approach achieves a higher edge user performance or cell capacity than with the current uncoordinated/coordinated approaches.

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.

Phase-Divided MAC Protocol for Integrated Uplink and Downlink Multiuser MIMO WLANs

Multiuser multiple-input and multiple-output (MU-MIMO) technology is regarded as one of the most promising technologies to cope with the enormous amount of network traffic in wireless local area networks. However, combining uplink and downlink MU-MIMO on the basis of a distributed coordination function causes serious channel resource loss because of collisions between uplink and downlink transmissions. In this paper, we propose a phase-divided medium access control (PD-MAC) protocol, in which a novel integrated frame structure is introduced that allocates two phases for uplink and downlink in a periodic cycle to remove contentions between uplink and downlink transmissions. PD-MAC also adopts an overhead mitigation scheme to reduce control overhead in user selection and employs an algorithm to balance uplink and downlink throughputs. An analytical model is developed to characterize the PD-MAC network throughput. Simulation results show that PD-MAC outperforms the previous unified MAC with regard to network throughput by 21.2%, and that the overhead mitigation scheme improves PD-MAC network throughput by 47.3%.

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 PSNR Optimized Cross Layer User Grouping and Resource Allocation for Uplink MU-MIMO OFDMA Video Communications

Wang et al. proposed cross layer resource allocation according to the channel state information of the physical layer and the video rate distortion of the application layer for uplink orthogonal frequency division multiple access (OFDMA) video transmission systems. Its object function is minimizing sum of all users’ video distortion. It shows significant improvement in average peak-signal-to-noise-ratio (PSNR) over the schemes which consider either the physical layer or application layer. However, minimizing sum of all users’ video distortion does not necessarily maximize average of all users’ PSNR. In this paper, we change the objective function for optimization to maximize average PSNR, and derive new optimal condition for cross layer subcarrier assignment for infinitesimal bandwidth increment. Furthermore, we extend to a multi-user multiple-input and multiple-output (MU-MIMO) uplink transmission system to improve information rate and thus video quality. We then propose an average PSNR optimized physical/application cross-layer user grouping and resource allocation for uplink MU-MIMO OFDMA video communications. The simulation results show that the proposed scheme has 4 dB PSNR gain over the previous Wang et al. cross-layer scheme and 5 dB PSNR gain over the previous physical layer only scheme for four users and SNR 12–18 dB.

Pilot contamination mitigation strategies in massive MIMO systems

Compared with the traditional multi-user MIMO (multiple-input and multiple-output), massive MIMO aims to serve tens of users with hundreds of antennas on each base station. All users can use the same time–frequency resources through space division multiple access, leading to vast improvement on spectral efficiency. However, to achieve the benefits, channel state information is usually required, and the acquisition is difficult in massive MIMO systems. Theoretically, each user should be assigned with orthogonal pilot sequences to avoid interference; however, due to the huge number of users (much more than available orthogonal pilot sequences) in service, pilot reuse in adjacent cells is inevitable, causing inter-cell interference. This phenomenon is often referred to as pilot contamination (PC) and is believed to be the fundamental limit on system capacity of massive MIMO systems. To solve this problem, many methods have been proposed since 2010, when the concept of massive MIMO was first proposed. In this study, the authors reviewed these methods, categorised them into four groups and compared their advantages and limitations. Although a survey on PC has been conducted by Elijah et al., where they tried to cover various aspects of the PC issue, their work focuses on the analysis of rationale and limitations of different contamination mitigation methods. Besides, performance evaluations are conducted and presented.

Robust Secure Transmission of Using Main-Lobe-Integration-Based Leakage Beamforming in Directional Modulation MU-MIMO Systems

In the paper, we make an investigation of robust beamforming for secure directional modulation in the multi-user multiple-input and multiple output (MU-MIMO) systems in the presence of direction angle measurement errors. When statistical knowledge of direction angle measurement errors is unavailable, a novel robust beamforming scheme of combining main-lobe-integration (MLI) and leakage is proposed to simultaneously transmit multiple different independent parallel confidential message streams to the associated multiple distinct desired users. The proposed scheme includes two steps: designing the beamforming vectors of the useful confidential messages and constructing artificial noise (AN) projection matrix. Here, in its first step, the beamforming vectors for the useful confidential messages of desired user k are given by minimizing the useful signal power leakage from main-lobe of desired user k to the sum of main-lobes of the remaining desired directions plus main-lobes of all eavesdropper directions. In its second step, the AN projection matrix is constructed by simultaneously maximizing the AN power leakage to all eavesdropper directions such that all eavesdroppers are disrupted seriously, where AN is viewed by the transmitter as a useful signal for eavedroppers. Due to independent beamforming vectors for different desired users, a more secure transmission is achieved. Compared with conventional non-robust methods, the proposed method can provide a significant improvement in bit error rate along the desired directions and secrecy-sum-rate towards multiple desired users without needing statistical property or distribution of angle measurement errors.