Abstract
The ubiquitous availability of wireless networks and devices provides a unique opportunity to leverage the corresponding communication signals to enable wireless sensing applications. In this article, we develop a new framework for environment sensing by opportunistic use of the mmWave communication signals. The proposed framework is based on a mixture of the conventional and Neural Network (NN) signal processing techniques for simultaneous counting and localization of multiple targets in the environment in a bi-static setting. In this framework, multi-modal delay, Doppler, angular features are first derived from the Channel State Information (CSI) estimated at the receiver, and then a transformer-based NN architecture exploiting attention mechanisms, called CSIformer, is designed to extract the most effective features for sensing. We also develop a novel post-processing technique based on Kullback-Leibler (KL) minimization to transfer knowledge between the counting and localization tasks, thereby simplifying the NN architecture. Our numerical results show accurate counting and localization capabilities that significantly outperform the existing works based on pure conventional signal processing techniques, as well as NN-based approaches. The simulation codes are available at: https://github.com/University-of-Surrey-Mahdi/Attention-on-the-Preambles-Sensing-with-mmWave-CSI.