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.

该论文介绍了一种名为实时感知和压缩网络（RSCNet）的新型网络，通过压缩通道状态信息（CSI）来减少通信开销，提高感知准确性。研究结果表明，RSCNet 相较于 SenseFi 等现有基准模型在感知准确性和通信成本等方面都有显著改进。

RSCNet：面向云端 WiFi 感知的动态 CSI 压缩

RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

Spectrum sensing plays a critical role in dynamic spectrum sharing, a
promising technology to address the radio spectrum shortage. In particular,
sensing of Orthogonal frequency division multiplexing (OFDM) signals, a widely
accepted multi-carrier transmission paradigm, has received paramount interest.
Despite various efforts, most conventional OFDM sensing methods suffer from
noise uncertainty, timing delay and carrier frequency offset (CFO) that
significantly degrade the sensing accuracy. To address these challenges, this
work develops two novel OFDM sensing frameworks drawing support from deep
learning networks. Specifically, we first propose a stacked autoencoder based
spectrum sensing method (SAE-SS), in which a stacked autoencoder network is
designed to extract the inherent features of OFDM signals. Using these features
to classify the OFDM user's activities, SAE-SS is much more robust to noise
uncertainty, timing delay, and CFO than the conventional OFDM sensing methods.
Moreover, SAE-SS doesn't require any prior information of signals (e.g., signal
structure, pilot tones, cyclic prefix) which are essential for the conventional
feature-based OFDM sensing methods. To further improve the sensing accuracy of
SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder
based spectrum sensing method using time-frequency domain signals (SAE-TF).
SAE-TF achieves higher sensing accuracy than SAW-SS at the cost of higher
computational complexity. Extensive simulation results show that both SAE-SS
and SAE-TF can achieve significantly higher sensing accuracy, compared with
state of the art approaches that suffer from noise uncertainty, timing delay
and CFO.

本文提出了两种基于深度学习网络的 OFDM 信号感知框架，分别为 SAE-SS 和 SAE-TF，与传统的 OFDM 感知方法相比，这两种方法可以更好地处理噪声不确定性、时延和载波频偏，同时提高了感知准确度。