PULSE-ELF: Temporal Channel Learning and Low-Overhead Feedback for Integrated Sensing and Communication in Commodity Wi-Fi

In this paper, we propose PULSE-ELF, a lightweight Integrated Sensing and Communication (ISAC) system whereby commodity Wi-Fi hardware enhances both the sensing capability and communication efficiency. To tackle the challenges in them, the work proposes to use temporal CFR learning to capture the dynamics of channel variations to perform robust sensing, and introduce a select input low-overhead channel feedback method to reduce the channel feedback cost by sending informative parts of channels only. To achieve this, the proposed framework combines temporal feature extraction, few-shot adaptation, and an efficient channel reconstruction, all of which are required for the practically deployable ISAC framework, relying on the ubiquitous Wi-Fi infrastructure. Through the experimental results, we show that the proposed approach can achieve more than 99% sensing accuracy and up to 96% reduction in feedback overhead, which is effective for scalable and resource-efficient Wi-Fi sensing and communication.