Deep Learning Channel Prediction for Transmit Power Control in Wireless Body Area Networks

Abstract

The general non-stationarity of the wireless body area network (WBAN) narrowband radio channel makes long-term prediction very challenging. However, long short-term memory (LSTM) is a deep learning recurrent neural network (RNN) architecture that is proposed here to learn these atypical radio channel dynamics and make channel predictions. Thus, here we propose an LSTM-based RNN channel prediction framework providing long-term channel prediction up to 2s with low error. To address practical scenarios where information packets are transmitted continuously, we outline a timing scheme, which enables the LSTM predictor to operate online. We employ the proposed method in transmit power control for everyday on-body, measured, WBAN channels. When compared with existing approaches, the proposed channel prediction reduces circuit power consumption significantly while improving communications reliability.