Resource-Efficient WiFi CSI Sensing via Exploiting the Age of Samples

Abstract

WiFi channel state information (CSI) sensing must coexist with data communications, which constrains the acquisition rate of fresh CSI measurements. To model this, we formulate CSI-based human activity and identity recognition under a sensing rate constraint that limits the fraction of time slots, within a measurement session, where CSI samples are available. This framework captures sensing-communication resource sharing and uncontrolled packet loss or traffic-driven irregularity. To satisfy the sensing constraint, two fixed CSI sampling policies are considered: a deterministic policy and a stochastic Bernoulli policy. We propose a low-cost age-aware WiFi sensing framework that explicitly incorporates sample freshness into the model training. The age of each retained CSI sample is first encoded and then fused with the CSI embedding via multiplicative fusion. On the NTU-Fi human activity recognition and person identification datasets, the proposed model consistently outperforms both a CSI-only baseline and the state-of-the-art time-aware attention model from the UniFi benchmark. For example, it yields up to a 10-percentage-point improvement over the UniFi method for person identification, with the largest gains observed under strict sensing budgets.