MU-MIMO Research Articles

AoD Based Joint User-Antenna Scheduling Schemes with Limited Feedback for Multi-User Massive MIMO FDD Systems

AoD Based Joint User-Antenna Scheduling Schemes with Limited Feedback for Multi-User Massive MIMO FDD Systems

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

ABSTRACTCompressed sensing is used for channel estimation in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems, but large‐scale networks face challenges regarding the antenna elements and spatial non‐stationarities. To enhance spectral efficiency in Multi User‐MIMO (MU‐MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter‐symbol interference rate along with inter‐carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi‐blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

Accelerating Iteratively Linear Detectors in Multi-User (ELAA-)MIMO Systems With UW-SVD

Accelerating Iteratively Linear Detectors in Multi-User (ELAA-)MIMO Systems With UW-SVD

Improved migration algorithms for uplink transmission secrecy sum rate maximization in MIMO-NOMA

The most significant technology that could assist future high-speed wireless systems in meeting their growing capacity demands is Non-Orthogonal Multiple Access (NOMA). Nevertheless, NOMA should strive to achieve a harmonious equilibrium between equitable distribution among users and optimal network performance. A wireless communication network is a system that enables connections with limited power resources, such as Internet of Things (IoT) devices. Horizontal multiplexing is a feature of MIMO networks that allows for the concurrent transmission of multiple data streams. Nevertheless, there is a correlation between space multiplexing and the advantages of diversity. The issue of Secrecy Sum Rate (SSR) is complex and challenging. To address this, a first-order Taylor estimate is employed to transform the initial problem into a more manageable convex problem. Striking the right balance to optimize network performance and maximize overall rate can be quite challenging, particularly in time-sensitive scenarios. The total rate that can be achieved is reduced due to interference from different users and the use of multiple transmitting antennas. To optimize performance and achieve the highest possible data transmission rate, it is crucial to employ efficient techniques for managing interference. These may include coding methods, adjustments to positioning, or coordinating multiple users. Thus, this work proposes the utilization of optimization to enhance SSR in the MIMONOMA uplink transmission framework. The Singular Value Decomposition (SVD) method is commonly used for the decomposition of channels in the MIMONOMA system. The primary goal of the developed MIMONOMA uplink transmission framework is to enhance the capacity of the Base Station (BS) by optimizing the power allocation measures of the near user and far user using an Improved Migration Algorithm (IMA). In addition, the SSR is optimized and multiple experimental validations are generated to appraise the suggested model. Ultimately, the suggested outcomes are contracted with conventional Orthogonal Multiple Access (OMA) and other traditional approaches to assess the efficacy of the designed model, and developed model given the SSR to be 30bit/s/Hz.

On the Spectral Efficiency of Multi-User Holographic MIMO Uplink Transmission

On the Spectral Efficiency of Multi-User Holographic MIMO Uplink Transmission

Multi-UAV Reinforcement Learning for Data Collection in Cellular MIMO Networks

Multi-UAV Reinforcement Learning for Data Collection in Cellular MIMO Networks

Uplink Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Uplink Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks

Minimizing Energy Consumption in Cell-Free Massive MIMO Networks

Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.

Achieving full mutualism with massive passive devices for multiuser MIMO symbiotic radio

Achieving full mutualism with massive passive devices for multiuser MIMO symbiotic radio

Analysis of Reduced Quantized Feedback-Based User-Antenna Scheduling Scheme for Multi-user Massive MIMO FDD Systems

Analysis of Reduced Quantized Feedback-Based User-Antenna Scheduling Scheme for Multi-user Massive MIMO FDD Systems

Intelligent Omni Surfaces Assisted Integrated Multi-Target Sensing and Multi-User MIMO Communications

Intelligent Omni Surfaces Assisted Integrated Multi-Target Sensing and Multi-User MIMO Communications

Federated Distillation in Massive MIMO Networks: Dynamic Training, Convergence Analysis, and Communication Channel-Aware Learning

Federated Distillation in Massive MIMO Networks: Dynamic Training, Convergence Analysis, and Communication Channel-Aware Learning

Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework

Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework

Pilot Assignment for Cell Free Massive MIMO Systems: A Successive Interference Cancellation Approach

In the contemporary era, considerable attention has been directed towards exploring cell-free massive multiple-input multiple-output (CF-M-MIMO) systems. This novel network paradigm has gained prominence as a potential solution for tackling the persistent challenge of inter-cell interference prevalent in traditional cellular MIMO networks. This study investigates a pilot assignment approach based on Successive Interference Cancellation (SIC) aimed at alleviating pilot contamination issues inherent in CF-M-MIMO systems. Through comprehensive numerical analyses and simulations, we demonstrate the efficacy and enhanced performance of the SIC-based approach competed with common random and greedy pilot assignment strategies. The proposed methodology addresses the critical challenge of pilot contamination, a phenomenon that severely impacts system performance and spectral efficiency. By iteratively decoding signals and canceling interference, the SIC algorithm optimizes pilot assignments, resulting in improved data rates and more efficient resource utilization. Our findings underscore the robustness and scalability of the SIC-based scheme across diverse system parameters and deployment scenarios, affirming its potential as a promising solution for enhancing the performance of CF-M-MIMO systems. Overall, this study contributes valuable insights into the design and optimization of pilot assignment strategies, offering a pathway for further research and development in the field of CF-M-MIMO systems.

A reliable downlink MIMO algorithm for mitigating the effect of user equipment mobility in multi-user MIMO in fifth-generation and beyond networks

A reliable downlink MIMO algorithm for mitigating the effect of user equipment mobility in multi-user MIMO in fifth-generation and beyond networks

Modeling and Performance Analysis of Vertical Heterogeneous Networks Under 3D Blockage Effects and Multiuser MIMO Systems

Modeling and Performance Analysis of Vertical Heterogeneous Networks Under 3D Blockage Effects and Multiuser MIMO Systems

Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

Clustering and Scheduling With Fairness Based on Information Rates for Cell-Free MIMO Networks

Clustering and Scheduling With Fairness Based on Information Rates for Cell-Free MIMO Networks