TY  - EJOU
AU  - Daoud, Mohammad Sh. 
AU  - Fatima, Areej 
AU  - Khan, Waseem Ahmad 
AU  - Khan, Muhammad Adnan 
AU  - Abbas, Sagheer 
AU  - Ihnaini, Baha 
AU  - Ahmad, Munir 
AU  - Javeid, Muhammad Sheraz 
AU  - Aftab, Shabib 

TI  - Joint Channel and Multi-User Detection Empowered with Machine Learning
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 70
IS  - 1
SN  - 1546-2226

AB  - The numbers of multimedia applications and their users increase with each passing day. Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems. In this article, a fuzzy logic empowered adaptive backpropagation neural network (FLeABPNN) algorithm is proposed for joint channel and multi-user detection (CMD). FLeABPNN has two stages. The first stage estimates the channel parameters, and the second performs multi-user detection. The proposed approach capitalizes on a neuro-fuzzy hybrid system that combines the competencies of both fuzzy logic and neural networks. This study analyzes the results of using FLeABPNN based on a multiple-input and multiple-output (MIMO) receiver with conventional partial opposite mutant particle swarm optimization (POMPSO), total-OMPSO (TOMPSO), fuzzy logic empowered POMPSO (FL-POMPSO), and FL-TOMPSO-based MIMO receivers. The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error, minimum mean channel error, and bit error rate.
KW  - Channel and multi-user detection; minimum mean square error; multiple-input and multiple-output; minimum mean channel error; bit error rate

DO  - 10.32604/cmc.2022.019295