Cell-Free Massive MIMO Systems Research Articles

An Analytical Reliability Performance Assessment of Cell-Free Massive MIMO Systems for 5G and Beyond Cellular Networks

In today‟s realm, the 5G cellular networks offer a new era of fully digital environment by connecting people anywhere anytime. This era requires inspiring demands to the networks in terms of spectral and energy efficiencies, low-latency and ultra-reliable communications. A major development to deal with these unparalleled demands is the introduction of Cell- Free Massive MIMO (CF mMIMO) systems to the cellular network. In recent years, mathematical foundations have been laid for a CF mMIMO communication system to evaluate the spectral and energy efficiencies given the number of access points. Various existing literatures have adopted mathematical models to formulate closed-form expressions for these efficiencies using uncorrelated Rayleigh fading for simple linear processing detectors. In this paper, we try to address a compelling and interesting question which has not received much attention lately. The question is how to evaluate the reliability of a connected user terminal in both a CF mMIMO and conventional mMIMO system? We answer this question by first analyzing the signal models for both systems assuming exact channel estimates and consider an uncorrelated Rayleigh fading channel model. The exact Bit Error Rate, as a reliability assessment, in closed-form are then derived based on strong mathematical foundations, which are simple to numerically evaluate in Matlab R2022, for MPSK and MQAM modulation schemes. The mathematical models presented in this work can be used in 5G and Beyond 5G networks to evaluate the distributions of error rates for a user given the number of Access points or Base Station antennas and modulation scheme. Machine Learning algorithms can be applied to the analytical expressions with a view to predict the error performance of a user terminal given the fading characteristic of the propagation environmen

Coverage Probability and Ergodic Capacity of Cell-Free Massive MIMO System

Coverage Probability and Ergodic Capacity of Cell-Free Massive MIMO System

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Distributed Hybrid Precoding for Edge-Computing-Assisted Cell-Free Massive MIMO Systems With Local CSI

Distributed Hybrid Precoding for Edge-Computing-Assisted Cell-Free Massive MIMO Systems With Local CSI

Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems

Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems

Outage Probability Analysis and Bit Allocation for Uplink Cell-Free Massive MIMO Systems With Low-Precision ADCs

Outage Probability Analysis and Bit Allocation for Uplink Cell-Free Massive MIMO Systems With Low-Precision ADCs

STAR-RIS Assisted Cell-Free Massive MIMO System Under Spatially-Correlated Channels

STAR-RIS Assisted Cell-Free Massive MIMO System Under Spatially-Correlated Channels

Heterogeneous Graph Neural Network for Power Allocation in Multicarrier-Division Duplex Cell-Free Massive MIMO Systems

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

Performance Analysis for User-Centric Cell-Free Massive MIMO Systems With Hardware Impairments and Multi-Antenna Users

Performance Analysis for User-Centric Cell-Free Massive MIMO Systems With Hardware Impairments and Multi-Antenna Users

Joint Access Points-User Association and Caching Placement Strategy for Cell-Free Massive MIMO Systems Based on Soft Actor-Critic Algorithm

Joint Access Points-User Association and Caching Placement Strategy for Cell-Free Massive MIMO Systems Based on Soft Actor-Critic Algorithm

A Survey of NOMA-Aided Cell-Free Massive MIMO Systems

The Internet of Everything is leading to an increasingly connected intelligent digital world. Envisaged sixth-generation wireless networks require new solutions and technologies due to stringent network requirements. The benefits of cell-free massive MIMO (CF-mMIMO) and non-orthogonal multiple access (NOMA) have brought substantial attention to these approaches as potential technologies for future networks. In CF-mMIMO, numerous distributed access points are linked to a central processing unit, which allocates the same time-frequency resources to a smaller group of users. On the other hand, NOMA can support more users than its orthogonal counterparts by utilizing non-orthogonal resource allocation. This paper provides a comprehensive review and survey of NOMA-aided CF-mMIMO (CF-mMIMO-NOMA). Specifically, we present a comprehensive review of massive MIMO, CF-mMIMO, and NOMA. We then present a state-of-the-art research review of CF-mMIMO-NOMA. Finally, we discuss the challenges and potential of combining CF-mMIMO-NOMA with other enabling technologies to enhance performance.

Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems

Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems

Optimal AP deployment in cell-free massive MIMO systems with LoS/NLoS transmissions: A stochastic geometry approach

Optimal AP deployment in cell-free massive MIMO systems with LoS/NLoS transmissions: A stochastic geometry approach

Joint Distributed Precoding and Beamforming for RIS-Aided Cell-Free Massive MIMO Systems

Joint Distributed Precoding and Beamforming for RIS-Aided Cell-Free Massive MIMO Systems

Energy-Efficient Optimization in User-Centric Cell-Free Massive MIMO Systems for URLLC With Finite Blocklength Communications

Energy-Efficient Optimization in User-Centric Cell-Free Massive MIMO Systems for URLLC With Finite Blocklength Communications

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

Distributed Combined Channel Estimation and Optimal Uplink Receive Combining for User- Centric Cell-Free Massive MIMO Systems

Distributed Combined Channel Estimation and Optimal Uplink Receive Combining for User- Centric Cell-Free Massive MIMO Systems

Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems

Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems

Fingerprint-based 3D Hierarchical Localization for Cell-Free Massive MIMO Systems

Fingerprint-based 3D Hierarchical Localization for Cell-Free Massive MIMO Systems