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.

翻译：WiFi赋能的物联网设备正从单纯的通信设备演变为传感仪器，这得益于信道状态信息的提取能力。然而，资源受限的物联网设备与深度神经网络的复杂性要求将CSI传输至云服务器进行感知。尽管该方案可行，但会带来显著的通信开销。在此背景下，本文提出了一种新颖的实时感知与压缩网络(RSCNet)，它能够实现对压缩后CSI的感知，从而降低通信开销。RSCNet通过优化由少量CSI帧组成的CSI窗口实现感知。一旦传输至云服务器，它便利用长短期记忆单元利用先前窗口的数据，从而同时提升感知精度与CSI重建质量。RSCNet巧妙平衡了CSI压缩与感知精度之间的权衡，以降低的通信成本实现了实时云基WiFi感知。数值结果表明，RSCNet相较于现有基准(如SenseFi)具有显著优势，在保持最小CSI重建误差的同时实现了97.4%的感知精度。数值结果还展示了所提RSCNet随CSI帧数变化的计算复杂度分析。