Adaptive Low-Overhead Channel Estimation Tracking in RIS-Assisted Systems

Efficient channel estimation is paramount for realizing the performance potential of Reconfigurable Intelligent Surface (RIS)-assisted wireless networks. This study introduces Adaptive Low-Overhead Channel Estimation Tracking (ALOCET), a novel solution to balance estimation accuracy with pilot resource efficiency, which is a long-standing challenge. Achieving precise channel knowledge in RIS systems is crucial; however, traditional methods demand excessive pilot overhead. ALOCET addresses this through a resource-conscious two-phase approach. An initial phase acquires a high-fidelity channel snapshot using dense pilots and comprehensive RIS configuration exploration. Subsequently, the low-overhead tracking phase employs a Cramér-Rao Lower Bound (CRLB)-driven dynamic RIS configuration selection. This mechanism selects optimal RIS configurations carrying more channel information to maintain accuracy, significantly reducing pilot demands. Simulation results, focusing on the tracking phase, demonstrate that ALOCET outperforms a representative two-time scale method, while substantially reducing pilot overhead. This adaptive approach provides a scalable and readily implementable framework for real-time channel estimation in future wireless networks.