通过回声状态网络实现WiFi感知
WiFi Sensing via Reservoir Computing
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中文总结 AI 辅助
研究在有限计算预算下的WiFi感知挑战，提出ReWiS框架，将CSI转换为微多普勒流，用图耦合回声状态网络编码，部署后微调读出。该框架在基准测试中表现良好，成本低、延迟低，为可部署WiFi感知提供实用设计。
中文摘要 AI 辅助
实际的WiFi感知必须在接入点（AP）、路由器和诸如ESP类设备等嵌入式物联网平台的有限计算预算下，处理时钟异步链路、跨域变化和部署后更新。回声状态网络（RC）在这种情况下很有吸引力，因为其时间编码器可以保持固定，只需优化和更新轻量级读出。为应对这些挑战，我们提出ReWiS，一种面向WiFi感知的回声状态网络框架，将信道状态信息（CSI）转换为具有常见、特定天线和差分运动线索的结构化微多普勒流，用图耦合回声状态网络对其进行编码，并在部署后通过冻结回声状态网络并仅用少量标记目标样本微调紧凑读出，以适应新域。在大规模WiFi感知基准测试中，ReWiS仅用0.72M可训练读出，域内宏F1达到89.2%，平均跨域宏F1达到82.0%，轻量级部署后适应后提高到88.5%，与近期深度基线相比具有竞争力，同时优化成本更低且CPU延迟更低。这些结果表明ReWiS为可部署的WiFi感知提供了实用的基于回声状态网络的设计，具有低功耗硬件实现的潜力。
英文摘要
Practical WiFi sensing must handle clock-asynchronous links, cross-domain variation, and post-deployment updating under the limited compute budget of access point (AP), router, and embedded Internet-of-Things platforms such as ESP-class devices. Reservoir computing (RC) is attractive in this setting because its temporal encoder can remain fixed while only a lightweight readout needs to be optimized and updated. To address these deployment challenges under tight compute budgets, we present ReWiS, a WiFi-sensing-oriented reservoir framework that transforms channel state information (CSI) into structured micro-Doppler streams with common, antenna-specific, and differential motion cues, encodes them with a graph-coupled reservoir, and adapts to a new domain after deployment by freezing the reservoir and fine-tuning only a compact readout with a few labeled target samples. On a large-scale WiFi sensing benchmark, ReWiS achieves 89.2% in-domain macro-F1 and 82.0% mean cross-domain macro-F1 with only a 0.72M trainable readout, improves to 88.5\% after lightweight post-deployment adaptation, and remains competitive with recent deep baselines evaluated under the same protocol, which achieve 87.5%-89.2% mean cross-domain macro-F1, while requiring lower optimization cost and lower CPU latency. These results indicate that ReWiS provides a practical reservoir-based design for deployable WiFi sensing, with further potential for low-power hardware realization.
