Joint Channel and Multi-User Detection Empowered with Machine Learning

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

doi:10.32604/cmc.2022.019295

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.

Highlights

Communication systems are ubiquitous and are plagued with the perennial problem of limited channel capacity
The transmitted information is calculated on different transmission paths that depend on the data conveyed by the multiple-input and multiple-output (MIMO) framework increments [10]
The Doppler frequency was set to 25 Hz, which corresponded to a transmitter using a 900 MHz carrier frequency and moving at a speed of 30 km/h

Summary

Introduction

Communication systems are ubiquitous and are plagued with the perennial problem of limited channel capacity. The multiple-input and multiple-output (MIMO) method has been used to improve the data rates of communication systems and resolve channel. An early system is known as a single input and single output used a single antenna for both the transmitter and receiver, which did not use the maximum bandwidth, which is one of the most important factors in communication systems. To overcome this issue, MIMO systems provide a solution through multiple antennas used at both ends [15]. We use fuzzy logic [26] to resolve this issue and improve the data and channel estimation process.

System Model

Proposed FLeABPNN-Based MIMO Receiver Model

Results and Discussion

Conclusion