EDChannel: channel prediction of backscatter communication network based on encoder-decoder
Pages 99 - 114
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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Index Terms
- EDChannel: channel prediction of backscatter communication network based on encoder-decoder
Index terms have been assigned to the content through auto-classification.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 September 2022
Accepted: 30 May 2022
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- Research-article
Funding Sources
- National Natural Science Foundation of China
- National Major Science and Technology Projects of China
- National Natural Science Foundation of China
- Applied Basic Research Program of Shanxi Province
- Key Technology Research and Development Program of Shandong
- Shanxi Province key technology and generic technology R &D project
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