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

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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.

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Funding

This paper supported by The General Object of National Natural Science Foundation under Grants(61972273); National Major Scientific Research Instrument Development Project (6202780085); National Natural Science Foundation of China(62102280); Fundamental Research Program of Shanxi Province(20210302124167); National Key R &D Project(2018YFB2200900); Shanxi Province key technology and generic technology R &D project(2020XXX007).

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Authors and Affiliations

  1. Shanxi, College of Data Science, Taiyuan University of Technology, Jinzhong, 030600, China

    Dengao Li & Shuang Xu

  2. Shanxi, College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China

    Yongxin Wen, Qiang Wang, Ruiqin Bai & Jumin Zhao

  3. Technology Research Centre of Spatial Information Network Engineering of Shanxi, Jinzhong, 030600, China

    Dengao Li & Shuang Xu

  4. Intelligent Perception Engineering Technology Centre of Shanxi, Jinzhong, 030600, China

    Yongxin Wen, Qiang Wang, Ruiqin Bai & Jumin Zhao

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  1. Dengao Li
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  2. Yongxin Wen
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  3. Shuang Xu
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  4. Qiang Wang
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  5. Ruiqin Bai
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  6. Jumin Zhao
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Correspondence to Jumin Zhao.

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Li, D., Wen, Y., Xu, S. et al. EDChannel: channel prediction of backscatter communication network based on encoder-decoder. Telecommun Syst 81, 99–114 (2022). https://doi.org/10.1007/s11235-022-00929-8

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