Beam Training for RIS-Aided ISAC Systems
2026-07-27 • Information Theory
Information Theory
AI summaryⓘ
The authors study a system that uses a smart surface (RIS) to improve both communication and sensing in future 6G networks. They create a method to quickly find the best communication beam while at the same time detecting the location of a target by analyzing echo signals. Their approach uses existing 5G codebooks and a smart search method to reduce training time. They also develop a way to accurately find the target’s position using signals from both the base station and the smart surface, and extend this to detect multiple targets. Their tests show the method works well for beam alignment and target localization.
Integrated Sensing and Communication (ISAC)Reconfigurable Intelligent Surface (RIS)Beam TrainingBeam AlignmentCodebookAuxiliary Beam Pair MethodTarget Localization6G NetworksEcho SignalMulti-target Detection
Authors
Jinho Yang, Hyeongtaek Lee, Junil Choi
Abstract
As a key technology for 6G, integrated sensing and communication (ISAC) is receiving considerable attention, and deploying a reconfigurable intelligent surface (RIS) can enhance both communication performance and sensing capability of ISAC by providing additional degrees of freedom. In this paper, we investigate a beam training framework for RIS-aided ISAC systems where beam alignment for a communication user equipment (UE) is conducted while simultaneously detecting a single target through its echo signal. Using codebooks constructed according to the principles of the 5G standard, we propose a partial search procedure that achieves low training overhead and mathematically show that this strategy is sufficient to identify a suitable codeword combination to serve the UE. By applying the auxiliary beam pair method, the target's angle information from the perspectives of the base station and RIS is obtained. Then, a high-accuracy closed-form localization is proposed based on the angle estimates, and we further extend the proposed technique to multi-target localization scenarios. Numerical results highlight the advantages of the proposed technique in the ISAC context, showing that the training procedure can effectively find a codeword combination and that the target localization technique outperforms the benchmarks.