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FlexScatter: Predictive Scheduling and Adaptive Rateless Coding for Wi-Fi Backscatter Communications in Dynamic Traffic Conditions

Xin He, Jingwen Xie, Aohua Zhang, Weiwei Jiang, Yujun Zhu, Tad Matsumoto

TL;DR

FlexScatter tackles the challenge of unstable, energy‑limited Wi‑Fi backscatter links by marrying a deep‑learning traffic predictor with an adaptive scheduling policy and a rateless LDPC coding framework. The core idea is to forecast Wi‑Fi traffic and selectively backscatter using a two‑bit ACK/NACK coordination, while the rateless LDPC code adapts to changing channel conditions through dynamic index/generator matrices and improved Belief Propagation decoding. Key contributions include the MSCAFM‑based traffic predictor (SAMSA‑Net), a DL‑driven scheduler with a practical threshold, and a novel rateless LDPC scheme that maintains reliability with minimal energy per bit. Experimental results demonstrate substantial BER improvements (up to ~30%), energy savings (up to ~7%), and utility gains (up to ~11%), indicating strong practical potential for predictive backscatter in unpredictable IoT traffic environments.

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 using 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%, improves energy efficiency by 7%, and increases 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.

FlexScatter: Predictive Scheduling and Adaptive Rateless Coding for Wi-Fi Backscatter Communications in Dynamic Traffic Conditions

TL;DR

FlexScatter tackles the challenge of unstable, energy‑limited Wi‑Fi backscatter links by marrying a deep‑learning traffic predictor with an adaptive scheduling policy and a rateless LDPC coding framework. The core idea is to forecast Wi‑Fi traffic and selectively backscatter using a two‑bit ACK/NACK coordination, while the rateless LDPC code adapts to changing channel conditions through dynamic index/generator matrices and improved Belief Propagation decoding. Key contributions include the MSCAFM‑based traffic predictor (SAMSA‑Net), a DL‑driven scheduler with a practical threshold, and a novel rateless LDPC scheme that maintains reliability with minimal energy per bit. Experimental results demonstrate substantial BER improvements (up to ~30%), energy savings (up to ~7%), and utility gains (up to ~11%), indicating strong practical potential for predictive backscatter in unpredictable IoT traffic environments.

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 using 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%, improves energy efficiency by 7%, and increases 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.

Paper Structure

This paper contains 28 sections, 6 equations, 11 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The State-of-the-Art
  3. Backscatter Communications
  4. Overview of Traffic Prediction Techniques
  5. Model-driven approaches
  6. Data-driven approaches
  7. System Architecture
  8. System Overview
  9. Wi-Fi Traffic Predictor
  10. Data Preparation
  11. Data Processing
  12. Data Modeling
  13. Scheduler
  14. Rateless LDPC Coding
  15. Index matrix construction
  16. ...and 13 more sections

Figures (11)

  • Figure 1: The proposed system model of Wi-FI backscatter communications with rateless encoding and traffic-based scheduling.
  • Figure 2: The workflow of the proposed method for Wi-Fi traffic prediction.
  • Figure 3: Analysis of interval time data with Pareto distribution.
  • Figure 4: BER performance with various initial exponent values.
  • Figure 5: BER comparison between rateless and QC-LDPC codes across different SNRs.
  • ...and 6 more figures