A Signal Detection Scheme Based on Deep Learning in OFDM Systems

TL;DR

This paper introduces a deep learning-based signal detection scheme for OFDM systems that leverages LSTM networks to improve detection performance without explicit channel state information estimation.

Contribution

The paper proposes a novel data-driven deep learning approach using LSTM for direct signal detection in OFDM systems, bypassing explicit CSI estimation.

Findings

Outperforms traditional methods in detection accuracy

Enhances channel estimation without explicit CSI

Demonstrates robustness in simulated wireless channels

Abstract

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.