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Location Prediction Empowered RIS Phase Optimization for ISAC System With High-Mobility
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AI
DOI：10.1109/LWC.2026.3678541.png)
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Abstract
En 中文
Reconfigurable Intelligent Surface (RIS) is a key technology for 6G communications. However, in high-mobility scenarios, traditional approaches based on statistical location information respond slowly, while those relying on instantaneous CSI are impractical due to excessive pilot overhead. To this end, we propose a location prediction empowerd RIS phase optimization (LP-RISPO) approach. Its core idea is to shift from passive “static optimization” to proactive “dynamic predictive optimization”. Specifically, it predicts the future motion state of user equipment (UE) based on real-time position, velocity, and uncertainty (covariance), and uses this prediction as prior knowledge to perform a one-time optimization of the RIS phase configuration. As a result, LP-RISPO significantly reduces pilot transmission and RIS phase reconfiguration frequency in high-mobility scenarios while improving the system’s average achievable rate, compared to benchmarks.
Keywords:
Reconfigurable intelligent surface (RIS)
LP-RISPO
error bound
optimization
Journal
I
IF:
5.5
Papers:
682
Citations:
0