AI 中文总结
研究RIS辅助的ISAC系统波束训练框架，利用5G标准码本提出低开销部分搜索过程，结合辅助波束对方法获取目标角度信息，进而提出高精度闭式定位并扩展到多目标场景，优势明显。
AI 中文摘要
作为6G的关键技术，集成感知与通信（ISAC）备受关注，部署可重构智能表面（RIS）能通过提供额外自由度增强ISAC的通信性能和感知能力。本文研究了一种用于RIS辅助的ISAC系统的波束训练框架，在为通信用户设备（UE）进行波束对准的同时，通过其回波信号检测单个目标。利用根据5G标准原理构建的码本，提出了一种实现低训练开销的部分搜索过程，并从数学上证明该策略足以识别适合UE的码字组合。通过应用辅助波束对方法，从基站和RIS的角度获取目标的角度信息。然后，基于角度估计提出了一种高精度闭式定位方法，并将该技术扩展到多目标定位场景。数值结果突出了所提技术在ISAC环境中的优势，表明训练过程能有效找到码字组合，且目标定位技术优于基准。
英文摘要
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.
Comments14 pages, 7 figures, accepted to IEEE Transactions on Wireless Communications
