Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

TL;DR

This paper introduces a deep learning framework for joint channel estimation and signal detection in OFDM systems, leveraging the correlation in wireless channels to improve accuracy and robustness over traditional methods.

Contribution

The paper presents two novel neural networks, CENet and CCRNet, that replace conventional estimation and detection methods in OFDM systems, enhancing performance and robustness.

Findings

CENet outperforms traditional interpolation-based channel estimation.

CCRNet achieves higher signal detection accuracy.

Networks are robust to parameter variations.

Abstract

In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme. Then, based on the outcome of the CENet, a Channel Conditioned Recovery Network (CCRNet) is designed to recover the transmit signal. Experimental results demonstrate that CENet and CCRNet achieve superior performance compared with conventional estimation and detection methods. In addition, both networks are shown to be robust to the variation of parameter chances, which makes them appealing for practical implementation.