A Signal Detection Scheme Based on Deep Learning in OFDM Systems
This addresses signal detection challenges in OFDM communication systems, but it is incremental as it applies an existing deep learning method (LSTM) to a specific domain.
The paper tackles channel estimation and signal detection in OFDM systems by developing a data-driven deep learning approach using LSTM, which directly restores transmitted signals without explicit channel state information. Simulation results show it outperforms traditional methods in improving performance.
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.