Ahmed K. Ali1, Jungpil Shin2,*, Yujin Lim3,*, Da-Hun Seong3
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4245-4278, 2025, DOI:10.32604/cmes.2025.073871
- 23 December 2025
Abstract Single-signal detection in orthogonal frequency-division multiplexing (OFDM) systems presents a challenge due to the time-varying nature of wireless channels. Although conventional methods have limitations, particularly in multi-input multioutput orthogonal frequency division multiplexing (MIMO-OFDM) systems, this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection. Specifically, we propose two hybrid architectures that integrate a convolutional neural network (CNN) with a recurrent neural network (RNN), namely, CNN-long short-term memory (CNN-LSTM) and CNN-bidirectional-LSTM (CNN-Bi-LSTM), designed to enhance signal detection performance in MIMO-OFDM systems. The proposed CNN-LSTM and CNN-Bi-LSTM architectures are… More >
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