Deep Learning Based Semi-Blind Tracking for Aging Wireless Communication Channels

Achal Dave, Sachit Kuhar, Ribhu Chopra

Abstract: The time-varying nature of the wireless propagation channel under high user mobilities, termed as channel aging, is a major performance impediment in many communication systems. In this paper, we discuss deep learning models for semi-blind channel estimation in a single input single output wireless communication system under channel aging. In our proposed scheme, we first use pilot based training to obtain initial channel estimates. Following this, we treat the detected symbols as pilots and perform further channel estimation using an Encoder-Decoder LSTM network for constant and sliding window schemes. To show the effectiveness of our method, we show the training capabilities of our models and the BER vs SNR graphs for multiple simulations. We discuss integrating these Encoder-Decoder LSTM models with deep learning enabled symbol detection techniques like the DetNet to further improve spectral efficiency. The Encoder-Decoder LSTM network gives us a low BER, with the moving window scheme outperforming the constant window scheme.