Open Access

ARTICLE

Joint Channel and Multi-User Detection Empowered with Machine Learning

by Mohammad Sh. Daoud¹, Areej Fatima², Waseem Ahmad Khan³, Muhammad Adnan Khan^4,5,*, Sagheer Abbas³, Baha Ihnaini⁶, Munir Ahmad³, Muhammad Sheraz Javeid⁷, Shabib Aftab³

1 College of Engineering, Al Ain University, Abu Dhabi, 112612, UAE
2 Department of Computer Science, Lahore Garrison University, Lahore, 54792, Pakistan
3 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
4 Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore, 54000, Pakistan
5 Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Seongnam, 13557, South Korea
6 Department of Computer Science, College of Science and Technology, Wenzhou Kean University, 325060, USA
7 Department of Computer Science, Hameeda Rasheed Institute of Science and Technology, Multan, 66000, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2022, 70(1), 109-121. https://doi.org/10.32604/cmc.2022.019295

Received 09 April 2021; Accepted 10 May 2021; Issue published 07 September 2021

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.

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Keywords

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