Time-Varying Channel Tracking Based on Compressed Sensing

Autonomous vehicles and integrated sensing and communication (ISAC) are two key use cases for the sixth generation (6G) communications. Since both are typically utilized in time-varying scenarios, they are strongly dependent on effective propagation channel tracking strategies. compressed sensing (CS) provides a powerful method for decomposing time-domain signals into sparse-domain representations. In this paper, a time-varying channel tracking method based on CS is proposed and its ability proven by realistic simulations. The proposed method not only provides a representation of the channel variation over time related to the physical characteristics of the propagation environment, but also enables to easily establish a trade-off between the reconstruction accuracy (related to the amount of sparse coefficients used) and computational complexity. The proposed method aims to establish the foundations for time-varying ISAC applications in the context of 6G.