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

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 channe...

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Bibliographic Details
Published inICC 2019 - 2019 IEEE International Conference on Communications (ICC) pp. 1 - 6
Main Authors Yang, Yizhou, Smith, David B., Seneviratne, Suranga
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Body area networks
Data models
Neural networks
Predictive models
Training
Training data
Wireless communication
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Summary: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.
ISSN:1938-1883
DOI:10.1109/ICC.2019.8761432