EDChannel: channel prediction of backscatter communication network based on encoder-decoder

Dengao Li (), Yongxin Wen (), Shuang Xu (), Qiang Wang (), Ruiqin Bai () and Jumin Zhao ()
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Dengao Li: Taiyuan University of Technology
Yongxin Wen: Taiyuan University of Technology
Shuang Xu: Taiyuan University of Technology
Qiang Wang: Taiyuan University of Technology
Ruiqin Bai: Taiyuan University of Technology
Jumin Zhao: Taiyuan University of Technology

Telecommunication Systems: Modelling, Analysis, Design and Management, 2022, vol. 81, issue 1, No 8, 99-114

Abstract: Abstract Backscatter communication networks have attracted much attention due to their small size and low power waste, but their spectrum resources are very limited and are often affected by link bursts. Channel prediction is a method to effectively utilize the spectrum resources and improve communication quality. Most channel prediction methods have failed to consider both spatial and frequency diversity. Meanwhile, there are still deficiencies in the existing channel detection methods in terms of overhead and hardware dependency. For the above reasons, we design a sequence-to-sequence channel prediction scheme. Our scheme is designed with three modules. The channel prediction module uses an encoder-decoder based deep learning model (EDChannel) to predict the sequence of channel indicator measurements. The channel detection module decides whether to perform a channel detection by a trigger that reflects the prediction effect. The channel selection module performs channel selection based on the channel coefficients of the prediction results. We use a commercial reader to collect data in a real environment, and build an EDChannel model based on the deep learning module of Tensorflow and Keras. As a result, we have implemented the channel prediction module and completed the overall channel selection process. The experimental results show that the EDChannel algorithm has higher prediction accuracy than the previous state-of-the-art methods. The overall throughput of our scheme is improved by approximately 2.9% and 14.1% over Zhao’s scheme in both stable and unstable environments.

Keywords: Backscatter communication; Channel prediction; Deep learning; Encoder-decoder (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11235-022-00929-8

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