Joint Channel and Multi-User Detection Empowered with Machine Learning

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 ad...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2022-01, Vol.70 (1), p.109-121
Main Authors: Sh. Daoud, Mohammad, Fatima, Areej, Ahmad Khan, Waseem, Adnan Khan, Muhammad, Abbas, Sagheer, Ihnaini, Baha, Ahmad, Munir, Sheraz Javeid, Muhammad, Aftab, Shabib
Format: Article
Language:English
Subjects:
Algorithms
Back propagation
Back propagation networks
Bit error rate
Errors
Fuzzy logic
Hybrid systems
Machine learning
Multimedia
Neural networks
Particle swarm optimization
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Summary: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.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2022.019295