Deep learning-based symbol detection for time-varying nonstationary channels

The highly dynamic channel (HDC) in an extremely dynamic environment mainly has fast time-varying nonstationary characteristics. In this article, we focus on the most difficult HDC case, where the channel coherence time is less than the symbol period. To this end, we propose a symbol detector based on a long short-term memory (LSTM) neural network. Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance. In addition, using the basic expansion model (BEM) as the preprocessing unit significantly reduces the number of neural network parameters. Finally, the simulation part uses the highly dynamic plasma sheath channel (HDPSC) data measured from shock tube experiments. The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

Automated summary

The article proposes a symbol detector based on LSTM neural network, which achieves better performance in highly dynamic environments with the use of BEM preprocessing, without the need for channel estimation or channel model information.