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.

翻译：Wi-Fi反向散射通信系统潜力巨大，但信号不稳定性和能量约束等挑战限制了其性能。本文提出FlexScatter系统，通过基于激励预测的调度策略和无速率编码来提升系统性能。首先，通过分析激励源的变异性构建Wi-Fi流量预测模型。随后，针对反向散射标签的低能耗需求，提出自适应传输调度算法，根据预测分析和时变信道条件调整传输策略。进一步结合低密度奇偶校验码与喷泉码的优势，开发了适用于动态信道条件的新型编解码算法。实验验证表明，与传统方法相比，FlexScatter可降低误码率最高达30%，提升能效7%，并提高整体系统效用11%。FlexScatter在能耗与通信效率间的平衡能力，使其成为依赖不可预测Wi-Fi流量的未来物联网应用的鲁棒解决方案。