Comm-Transformer: A Robust Deep Learning-Based Receiver for OFDM System Under TDL Channel
Yihang Xie, Kah Chan Teh, Alex C. Kot
Abstract
In this paper, we propose a deep learning (DL) based receiver named comm-transformer network (Comm-Trans Net), which is robust for different sub-types of tapped delay line (TDL) channels. The novel Comm-Trans Net considers the attention mechanism to compensate for the multi-path fading effect for different orthogonal frequency-division multiplexing (OFDM) subcarriers. We propose a novel positional encoding method for each OFDM subcarrier, which uses the attention mechanism to offset the deep fading effect. In particular, our proposed Comm-Trans Net serves as an integrated DL-based receiver for unknown channel conditions. Our results show that the proposed Comm-Trans Net can outperform the bit-error rate (BER) performance compared to minimum mean-square error (MMSE) channel estimation with generalized approximate message passing (GAMP) receiver, and also outperforms the state-of-the-art DL-based receivers. Moreover, our proposed Comm-Trans Net is robust for multiple communication scenarios ranging from the TDL-A channel to the TDL-E channel with different levels of non-line-of-sight (NLoS) and line-of-sight (LoS) components. Also, the computational complexity of the proposed Comm-Trans Net is studied, which shows that the attention mechanism for channel positional encoding is a cost-effective solution to improve the quality of service.