Slim-Sense: A Resource Efficient WiFi Sensing Framework towards Integrated Sensing and Communication

Abstract

With the growing use cases of CSI-based WiFi sensing, future WiFi networks are moving towards integrating sensing and communication (ISAC) by sharing the same frequency resources between data communication and WiFi sensing. However, it is known that WiFi sensing is detrimental to WiFi communication due to its expensive use of frequency resources for collecting CSI samples, limiting its effectiveness in ISAC. To address this challenge, we propose Slim-Sense, a novel approach to resource saving while maximizing the sensing accuracy. We first demonstrate that it is possible to perform accurate WiFi sensing without using the entire bandwidth. In fact, we can obtain close to maximum accuracy while utilizing only 24.42% of the bandwidth and 25% of the antennas. Obtaining such accuracy at low bandwidth requires the selection of the antennas and bandwidth that are most relevant for sensing activities. One of Slim-Sense's highlights is using a novel approach consisting of a Sparse Group Regularizer (SGR) and Hierarchical Reinforcement learning (HRL) technique to select the minimum optimal bandwidth resources for sensing while maximizing sensing accuracy. Considering the stochastic nature of the sensing environment and the difference in requirements of different sensing applications, Slim-Sense provides an environment and application-specific bandwidth resources for sensing. We evaluate Slim-Sense with four different WiFi CSI datasets, each varying in sensing environment and application, including one we collected in 46 different environmental settings. The experimental evaluation shows that Slim-Sense saves up to 92.9% resources while incurring < 5% reduction in sensing accuracy compared to using entire spectrum resources. We show that Slim-Sense is generalized to different environments and sensing models. Compared to the state-of-art solution, Slim-Sense outperforms and achieves a maximum improvement of 28.75% in resource-saving and 42.18% in sensing accuracy.