MU-MIMO Research Articles

Blind Signal Reception in Downlink Generalized Spatial Modulation Multiuser MIMO System Based on Minimum Output Energy

Blind Signal Reception in Downlink Generalized Spatial Modulation Multiuser MIMO System Based on Minimum Output Energy

Double-RIS Aided Multi-User MIMO Communications: Common Reflection Pattern and Joint Beamforming Design

Double-RIS Aided Multi-User MIMO Communications: Common Reflection Pattern and Joint Beamforming Design

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO Networks

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO Networks

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Generalized Optimization Method of Placement and Beamforming Design for Multiuser MIMO UAV Communications

Generalized Optimization Method of Placement and Beamforming Design for Multiuser MIMO UAV Communications

Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.

Secured Performance Analysis of Double Scattering MIMO Network

Secured Performance Analysis of Double Scattering MIMO Network

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

Performance analysis of multiple RISs aided multi-user mmWave MIMO systems

Performance analysis of multiple RISs aided multi-user mmWave MIMO systems

Variational Optimization for Sustainable Massive MIMO Base Station Switching.

Massive MIMO networks are a promising technology for achieving ultra-high capacity and meeting future wireless service demand. Massive MIMO networks, on the other hand, consume intensive energy. As a result, energy-efficient operation of massive MMO networks became a requirement rather than a luxury. Many NP-hard concavity search algorithms for optimal base station switching on-off scheme have been developed. This paper demonstrates the formulation of massive MIMO networks energy efficiency as a constrained variational problem. Our proposed method solution's uniqueness and boundedness are demonstrated and proven. The developed system is a total energy optimization problem formulation. Furthermore, the order in which the base stations are switched on and off is specified for minimal handover overhead signaling and fair user capacity sharing. Results showed that variational optimization yielded optimal base station switching on and off with considerable energy saving achieved and maintaining the user capacity demand. Moreover, the proposed base station selection criteria provided suboptimal handover overhead signaling.

Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals

Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

Hybrid Precoding Design in Multi-User mmWave Massive MIMO Systems for BER Minimization

Hybrid Precoding Design in Multi-User mmWave Massive MIMO Systems for BER Minimization

QRD-based precoding design for multiuser massive MIMO null-space expansion

QRD-based precoding design for multiuser massive MIMO null-space expansion

Joint Design of Antenna Selection and Resource Allocation in Massive MIMO‐Based Ultra Dense Heterogeneous Networks

Fifth generation (5G) communication network is anticipated to satisfy the need for higher data rates, less latency, ubiquitous connectivity with minimum consumption of energy and acceptable quality of Service (QoS). The massive MIMO and heterogeneous network (HetNet) has evolved as a promising technology to address the aforementioned challenges. In the proposed framework, the massive MIMO technology at the macro base station and full duplex (FD) technology enabled at the small cell base station in HetNet are investigated. An optimal antenna selection strategy, user association (UA), and power allocation (PA) in a massive MIMO‐based HetNet with FD‐enabled small cells are proposed to optimize system sum rate. Initially, the objective function of the optimization problem is nonconvex and a mixed integer nonlinear programming problem. Then, the solution is obtained by transforming the problem into convex problem utilizing Lagrangian decomposition. Simulation results manifest the effect of the number of base station antennas, number of small cell base stations, self‐interference cancellation factor, and channel state information on system sum rate. Moreover, it is evident that the propounded algorithm is effective in terms of optimizing the system sum rate when compared to the conventional maximum signal‐ to ‐ interference ‐ plus ‐noise ‐ ratio (SINR) algorithm.

Location-driven beamforming for massive multi-user MIMO systems

Location-driven beamforming for massive multi-user MIMO systems

Learning Power Allocation and Channel Estimation for Superimposed Pilot-Assisted Multiuser MIMO

Learning Power Allocation and Channel Estimation for Superimposed Pilot-Assisted Multiuser MIMO

A Bi-Directional Distributed Multi-User MIMO Testbed Using Digital Sigma-Delta-Over-Fiber

A Bi-Directional Distributed Multi-User MIMO Testbed Using Digital Sigma-Delta-Over-Fiber

Exploiting Zero Forcing and Block Diagonalization Precoding for Multi-User MIMO ISAC: Bridging Communications and Sensing

Exploiting Zero Forcing and Block Diagonalization Precoding for Multi-User MIMO ISAC: Bridging Communications and Sensing

Improving Signal Detector by Precoding in Uplink Multiuser MIMO System

Improving Signal Detector by Precoding in Uplink Multiuser MIMO System