Joint Channel and Multi-User Detection Empowered with Machine Learning
- Authors
- Daoud, M.Sh.; Fatima, A.; Khan, W.A.; Khan, M.A.; Abbas, S.; Ihnaini, B.; Ahmad, M.; Javeid, M.S.; Aftab, S.
- Issue Date
- Jan-2022
- Publisher
- TECH SCIENCE PRESS
- Keywords
- Bit error rate; Channel and multi-user detection; Minimum mean channel error; Minimum mean square error; Multiple-input and multiple-output
- Citation
- CMC-COMPUTERS MATERIALS & CONTINUA, v.70, no.1, pp.109 - 121
- Journal Title
- CMC-COMPUTERS MATERIALS & CONTINUA
- Volume
- 70
- Number
- 1
- Start Page
- 109
- End Page
- 121
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82189
- DOI
- 10.32604/cmc.2022.019295
- ISSN
- 1546-2218
- 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), 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. © 2021 Tech Science Press. All rights reserved.
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