@Article{cmc.2022.019295,
AUTHOR = {Mohammad Sh. Daoud, Areej Fatima, Waseem Ahmad Khan, Muhammad Adnan Khan, Sagheer Abbas, Baha Ihnaini, Munir Ahmad, Muhammad Sheraz Javeid, Shabib Aftab},
TITLE = {Joint Channel and Multi-User Detection Empowered with Machine Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {70},
YEAR = {2022},
NUMBER = {1},
PAGES = {109--121},
URL = {http://www.techscience.com/cmc/v70n1/44393},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2022.019295}
}