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 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), totalOMPSO (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.
Computers, Materials and Continua
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Daoud, Mohammad Sh; Fatima, Areej; Khan, Waseem Ahmad; Khan, Muhammad Adnan; Abbas, Sagheer; Ihnaini, Baha; Ahmad, Munir; Javeid, Muhammad Sheraz; and Aftab, Shabib, "Joint channel and multi-user detection empowered with machine learning" (2021). Kean Publications. 1068.