Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

Mohamed Hassan Essai Ali (), Ali R. Abdellah (), Hany A. Atallah, Gehad Safwat Ahmed, Ammar Muthanna and Andrey Koucheryavy
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Mohamed Hassan Essai Ali: Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt
Ali R. Abdellah: Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt
Hany A. Atallah: Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt
Gehad Safwat Ahmed: Department of Electrical Engineering, Luxor Academy of Engineering and Technology, Qena 83513, Egypt
Ammar Muthanna: Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, St. Petersburg 193232, Russia
Andrey Koucheryavy: Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, St. Petersburg 193232, Russia

Mathematics, 2023, vol. 11, issue 15, 1-19

Abstract: This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

Keywords: LSTM; peephole LSTM; GRU; deep learning; channel estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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