RSCNet：面向云端WiFi感知的动态CSI压缩
RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing
AI总结：
针对云端WiFi感知中CSI传输开销大的问题，提出RSCNet，通过跨CSI窗口优化与云端LSTM利用历史窗口数据，在压缩CSI的同时保持高感知精度与低重建误差。
摘要：
支持WiFi的物联网（IoT）设备正借助信道状态信息（CSI）提取能力，从单纯的通信设备演变为感知仪器。然而，资源受限的IoT设备以及深度神经网络的复杂性，使得必须将CSI传输到云服务器进行感知。尽管这种做法可行，却会带来巨大的通信开销。在此背景下，本文开发了一种新颖的实时感知与压缩网络（Real-time Sensing and Compression Network，RSCNet），它能够利用压缩后的CSI进行感知，从而降低通信开销。RSCNet支持在由少量CSI帧组成的CSI窗口上进行优化。一旦数据传输到云服务器，它便采用长短期记忆（LSTM）单元来利用先前窗口的数据，从而同时提升感知精度和CSI重建效果。RSCNet巧妙平衡了CSI压缩与感知精度之间的权衡，进而以更低的通信成本简化实时云端WiFi感知。数值结果证明了RSCNet相较于SenseFi等现有基准方法的优势，其在实现97.4%感知准确率的同时具有最小的CSI重建误差。数值结果还给出了所提出的RSCNet随CSI帧数量变化的计算分析。
英文摘要：
WiFi-enabled Internet-of-Things (IoT) devices are evolving from mere communication devices to sensing instruments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI; thereby reducing the communication overheads. RSCNet facilitates optimization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4% with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
