Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

TL;DR

This paper demonstrates that deep learning can effectively perform channel estimation and signal detection in OFDM systems, handling complex distortions and interferences more robustly than traditional methods.

Contribution

The paper introduces an end-to-end deep learning approach that implicitly estimates CSI and directly recovers transmitted symbols in OFDM, outperforming conventional techniques under challenging conditions.

Findings

Deep learning achieves comparable performance to MMSE estimators.

The approach is more robust with fewer pilots and no cyclic prefix.

It effectively handles nonlinear clipping noise and channel distortions.

Abstract

This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.