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IJEETC 2024 Vol.13(1): 67-79
doi: 10.18178/ijeetc.13.1.67-79

Deep Learning-Based SC-FDMA Channel Equalization

Mohamed A. Mohamed1,2,*, Hassan A. Hassan1,2, Mohamed H. Essai1, Hamada Esmaiel2, Ahmed S. Mubarak2, and Osama A. Omer2
1. Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt
2. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Abulrish 81542, Egypt
Email: mohammed.anbar@azhar.edu.eg (M.A.M.), hassanali2720.el@azhar.edu.eg (H.A.H.), mhessai@azhar.edu.eg (M.H.E.), h.esmaiel@aswu.edu.eg (H.E.), ahmed.soliman@aswu.edu.eg (A.S.M.), omer.osama@aswu.edu.eg (O.A.O.)
*Corresponding author

Manuscript received July 2, 2023; revised August 22, 2023; accepted September 8, 2023; published February 2, 2024.

Abstract—It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

Index Terms—Deep learning, channel equalization, Long Short-Term Memory (LSTM), Quadrature Phase-Shift Keying (QPSK), recurrent neural network, single-carrier frequency-division multiple access

Cite: Mohamed A. Mohamed, Hassan A. Hassan, Mohamed H. Essai, Hamada Esmaiel, Ahmed S. Mubarak, and Osama A. Omer, "Deep Learning-Based SC-FDMA Channel Equalization," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 13, No. 1, pp. 67-79, 2024. doi: 10.18178/ijeetc.13.1.67-79

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.