LAUSR

The ambitious goals of the sixth-generation (6G) mobile communication networks require efficient acquisition of channel state information (CSI) for large-dimensional wireless channels. To this end, one may exploit the new opportunities of the significantly enhanced sensing capabilities and the paradigm shift from environment-unaware communication to environment-aware communication. However, existing environment-aware techniques mainly assume quasi-static environments, which become ineffective for highly dynamic scenarios. To address such issues, in this letter, we decompose the wireless environment into quasi-static and dynamic components and propose an efficient channel estimation method by integrating channel knowledge map (CKM) and dynamic sensing information. Specifically, CKM is a database storing location-specific channel knowledge that provides quasi-static channel information. By integrating CKM with real-time sensed dynamic object locations, an effective low-overhead channel estimation technique is developed. Analysis reveals that CKM not only utilizes user location information but also can effectively incorporate dynamic scatterer locations, exploring the impact of dynamic scatterers on the channel. Simulation results demonstrate that the proposed method significantly improves communication performance by effectively utilizing both CKM and dynamic environment information.