FPNet: Joint AI for CSI Feedback and High-Accuracy Positioning in Wi-Fi Systems

Wi-Fi sensing has gained substantial attention in recent years, particularly for indoor positioning applications. Conventional indoor wireless positioning methods typically assume access to complete channel state information (CSI), which is often impractical in real-world systems. This paper proposes a novel approach for indoor positioning utilizing a compressed beamforming feedback matrix (BFM), which is inherently integrated into the Wi-Fi protocol for meeting CSI feedback requirements. We introduce FPNet, a joint neural network model, in which the BFM is compressed into codewords by an encoder at the client station (STA) side. These codewords are subsequently transmitted to the access point (AP) side, which features a decoder and a positioning network responsible for reconstructing the codewords and estimating positions. The encoder and decoder are trained end-to-end. FPNet is implemented with standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental results demonstrate that this approach not only improves net throughput by up to 22.92% but also achieves positioning accuracy exceeding 97%.