Transformer-empowered receiver design of OFDM communication systems

Abstract

With deep learning, we perform channel estimation and signal detection in massive Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems in this paper. Specifically, we design and extend the basic framework of receivers for MIMO-OFDM systems in an end-to-end approach. A Transformer-based MIMO-OFDM receiver called TCD-Receiver is proposed, which introduces a multi-attention mechanism to learn the channel characteristics by introducing a generic and flexible Transformer network structure. The network parameters are updated based on the relationship between the received signal and the original signal, where the final signal information is obtained without explicit channel estimation and the predicted transmit bits are directly output. The experimental results show that the TCD-Receiver proposed can effectively solve the channel distortion and detect the transmitted signals compared with the traditional communication receivers, and its performance can be comparable to that of the traditional OFDM receivers, and it also has obvious advantages in combating the complex and difficult-to-model channel environment as well as the nonlinear interference factors.