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

TLDR

The letter investigates using deep learning for end‑to‑end channel estimation and signal detection in OFDM systems. A deep‑learning model is trained offline on simulated channel statistics and then applied online to implicitly estimate CSI and directly recover transmitted symbols, bypassing explicit CSI estimation. Simulation shows the model achieves performance comparable to a minimum‑mean‑square‑error estimator and outperforms conventional methods when pilots are scarce, the cyclic prefix is omitted, or clipping noise is present, demonstrating its promise for complex channel distortion and interference.

Abstract

This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect/recover the transmitted symbols using the estimated CSI, the proposed 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 simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference.