Channel State Information (CSI) provides a detailed description of the wireless channel and has been widely adopted for Wi-Fi sensing, particularly for high-precision indoor positioning. However, complete CSI is rarely available in real-world deployments due to hardware constraints and the high communication overhead required for feedback. Moreover, existing positioning models lack mechanisms to detect when users move outside their trained regions, leading to unreliable estimates in dynamic environments. In this paper, we present FPNet, a unified deep learning framework that jointly addresses channel feedback compression, accurate indoor positioning, and robust anomaly detection (AD). FPNet leverages the beamforming feedback matrix (BFM), a compressed CSI representation natively supported by IEEE 802.11ac/ax/be protocols, to minimize feedback overhead while preserving critical positioning features. To enhance reliability, we integrate ADBlock, a lightweight AD module trained on normal BFM samples, which identifies out-of-distribution scenarios when users exit predefined spatial regions. Experimental results using standard 2.4 GHz Wi-Fi hardware show that FPNet achieves positioning accuracy above 97% with only 100 feedback bits, boosts net throughput by up to 22.92%, and attains AD accuracy over 99% with a false alarm rate below 1.5%. These results demonstrate FPNet's ability to deliver efficient, accurate, and reliable indoor positioning on commodity Wi-Fi devices.

翻译：信道状态信息（CSI）提供了无线信道的详细描述，并已广泛应用于Wi-Fi感知，特别是高精度室内定位。然而，由于硬件限制和反馈所需的高通信开销，在实际部署中很少能获得完整的CSI。此外，现有定位模型缺乏检测用户何时移出训练区域的机制，导致在动态环境中产生不可靠的估计。本文提出FPNet，一个统一的深度学习框架，联合解决信道反馈压缩、精确室内定位和鲁棒的异常检测（AD）问题。FPNet利用波束赋形反馈矩阵（BFM）——一种由IEEE 802.11ac/ax/be协议原生支持的压缩CSI表示——在最小化反馈开销的同时保留关键的定位特征。为了增强可靠性，我们集成了ADBlock，一个在正常BFM样本上训练的轻量级AD模块，用于在用户离开预定义空间区域时识别分布外场景。使用标准2.4 GHz Wi-Fi硬件的实验结果表明，FPNet仅需100个反馈比特即可实现97%以上的定位精度，将网络吞吐量提升高达22.92%，并以低于1.5%的误报率获得超过99%的AD准确率。这些结果证明了FPNet在商用Wi-Fi设备上实现高效、精确且可靠的室内定位的能力。