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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/19679

Title: FlexScatter: Predictive Scheduling and Adaptive Rateless Coding for Wi-Fi Backscatter Communications in Dynamic Traffic Conditions
Authors: He, Xin
Xie, Jingwen
Zhang, Aohua
Jiang, Weiwei
Zhu, Yujun
Matsumoto, Tad
Keywords: Wi-Fi Backscatter Communication Systems
Traffic Prediction
Coding Algorithm
Transmission Scheduling
Deep Learning
Issue Date: 2025-03-04
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE Transactions on Green Communications and Networking
Start page: 1
End page: 1
DOI: 10.1109/TGCN.2025.3547569
Abstract: The potential of Wi-Fi backscatter communications systems is immense, yet challenges such as signal instability and energy constraints impose performance limits. This paper introduces FlexScatter, a Wi-Fi backscatter system featuring a designed scheduling strategy based on excitation prediction and rateless coding to enhance system performance. Initially, a Wi-Fi traffic prediction model is constructed by analyzing the variability of the excitation source. Then, an adaptive transmission scheduling algorithm is proposed to address the low energy consumption demands of backscatter tags, adjusting the transmission strategy according to predictive analytics and taming channel conditions. Furthermore, leveraging the benefits of low-density parity-check (LDPC) and fountain codes, a novel coding and decoding algorithm is developed, which is tailored for dynamic channel conditions. Experimental validation shows that FlexScatter reduces bit error rates (BER) by up to 30%, enhances energy efficiency by 7%, and overall system utility by 11%, compared to conventional methods. FlexScatter’s ability to balance energy consumption and communication efficiency makes it a robust solution for future IoT applications that rely on unpredictable Wi-Fi traffic.
Rights: This is the author's version of the work. Copyright (C) 2024 IEEE. IEEE Transactions on Green Communications and Networking. DOI: 10.1109/TGCN.2025.3547569. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/19679
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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