Shared Cluster-Based Communication Channel Reconstruction from Sensing Channels

Accurate channel state information is essential for the performance of modern wireless communication systems. Conventional channel estimation typically relies on uplink Sounding Reference Signals (SRSs), which can introduce considerable overhead and power consumption, particularly in high-mobility or resource-constrained scenarios. To alleviate this burden, this paper explores an alternative approach that leverages sensing channel information to assist communication channel reconstruction. A shared cluster concept is introduced to capture the correlation between sensing and communication channels, and a sharing probability function is derived through statistical analysis of ray tracing simulation data across multiple scenarios. The shared cluster parameters extracted from the sensing channels are integrated into a cluster-based channel modeling framework to reconstruct the downlink communication channel. A deterministic simulation platform is developed using the Sionna ray tracing library, and the K-Power-Means algorithm is employed for multipath clustering. Simulation results demonstrate that the reconstructed channel closely matches the original channel in terms of the power delay profile and the root mean square delay spread, with mean values of 84.16 ns and 73.52 ns, respectively. The proposed method offers a promising supplementary approach for channel acquisition in scenarios where frequent SRS transmission is undesirable, and provides insights for future sensing-assisted communication system design.