arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.15919eess.SPcs.ITmath.IT
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基于可重构智能表面的成像与通信一体化

Integrated Imaging and Communication with Reconfigurable Intelligent Surfaces

Hao Luo, Ahmed Alkhateeb

AI总结:

本研究提出RIS辅助的成像通信一体化系统,通过环境成像构建深度图提取用户属性,以此低开销设计RIS交互向量与波束策略,在波束成形增益相当的情况下将训练开销降至穷举方案的千分之一。

摘要:

可重构智能表面(Reconfigurable Intelligent Surfaces,RIS)凭借其大量天线,为高空间分辨率成像提供了颇具吸引力的机遇。本文提出了一种新型的RIS辅助成像与通信一体化系统,该系统可通过对周围环境的成像,降低通信过程中的RIS波束训练开销。具体而言,系统将RIS用作无线成像设备,构建包含移动用户在内的环境场景深度图。随后,我们开发了一种用户检测算法,该算法可从深度图中扣除背景并提取移动用户的属性。利用这些属性,我们以低开销设计了RIS交互向量和波束选择策略。仿真结果表明,所提方法能够实现与最优/穷举波束选择方案相当的波束成形增益,而所需的波束训练开销仅为后者的千分之一。

英文摘要:

Reconfigurable intelligent surfaces, with their large number of antennas, offer an interesting opportunity for high spatial-resolution imaging. In this paper, we propose a novel RIS-aided integrated imaging and communication system that can reduce the RIS beam training overhead for communication by leveraging the imaging of the surrounding environment. In particular, using the RIS as a wireless imaging device, our system constructs the scene depth map of the environment, including the mobile user. Then, we develop a user detection algorithm that subtracts the background and extracts the mobile user attributes from the depth map. These attributes are then utilized to design the RIS interaction vector and the beam selection strategy with low overhead. Simulation results show that the proposed approach can achieve comparable beamforming gain to the optimal/exhaustive beam selection solution while requiring 1000 times less beam training overhead.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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