Deep learning has emerged as a promising paradigm for physical layer processing in wireless communication systems. In this paper, we present a comprehensive implementation and evaluation of DeepRx, a fully convolutional neural network (CNN) that replaces the entire receiver processing chain in a 5G-compliant orthogonal frequency-division multiplexing (OFDM) system. The proposed architecture jointly performs channel estimation, equalization, and soft demapping by processing the frequency-domain received signal directly into bit-level log-likelihood ratios (LLRs). We construct a three-component input tensor comprising the received signal, known pilot symbols, and raw channel estimates, enabling the network to leverage both pilot and unknown data symbols for improved channel tracking. Through extensive simulations using 3GPP-defined tapped delay line (TDL) channel models with varying signal-to-noise ratios (0-24 dB) and Doppler shifts (10-500 Hz), we demonstrate that DeepRx achieves up to 1.5x lower bit error rate (BER) compared to a traditional least squares/linear minimum mean square error (LS/LMMSE) receiver. Per-bit analysis reveals that the performance gains are predominantly attributed to improved amplitude estimation of the least significant bits, suggesting that the network implicitly learns a blind equalization strategy. The complete source code is available at: https://github.com/G-ALI007/DeepRx-OFDM-PyTorch
Ghader Ali (Sat,) studied this question.