LAUSR

Device-free wireless sensing (DFWS) has drawn lots of attention due to its potential application in the fields of human–computer interaction and smart home. Deep networks-based DFWS technique has achieved excellent sensing performance. However, network complexity limits its deployment on resource-limited sensing devices. A feasible way is to implement a simple network to accomplish the DFWS task. However, the sensing performance will drop dramatically due to its limited learning ability. In this article, to realize lightweight DFWS with acceptable performance, we propose an information-for-complexity strategy to promote the learning ability of the simple network. We leverage knowledge distillation framework to explore external information to augment the extrinsic learning ability, and utilize multiscale receptive fields to explore the internal information to augment the intrinsic learning ability. Extensive experiments on a 77 GHz mmWave testbed show that the performance degradation of the lightweight DFWS system is within 3%, while the complexity decreases remarkably.