可重构智能表面（RIS）辅助的雷达-通信共存：考虑信道不确定性的检测分析
RIS-Assisted Radar-Communication Coexistence: Detection Analysis with Channel Uncertainties
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中文总结 AI 辅助
本文针对未协同RCC场景，开发RIS辅助RCC框架，推导两种无需跟踪雷达干扰相位的实用ML检测器，验证了非相干检测器的实用性。
中文摘要 AI 辅助
可重构智能表面（RIS）已成为提升雷达-通信共存（RCC）场景中通信可靠性的极具前景的技术，尤其适用于位于雷达禁区内的通信用户（CU），该区域仅允许雷达运行，且雷达与通信系统未实现协同。在此类场景中，通信用户可能遭受强雷达干扰，其相位随机且难以跟踪，同时还面临从基站获取准确信道状态信息（CSI）的困难。当存在RIS相位误差时，这些挑战会变得更为严峻，限制了传统相干检测方法的适用性。受这些实际限制的驱动，本文开发了一种适用于未协同RCC场景下通信用户的RIS辅助RCC框架。在该框架内，推导了两种实用的基于最大似然（ML）的检测器，二者均无需跟踪雷达干扰相位：1）非相干检测器，无需瞬时CSI，且纳入了RIS相位不确定性；2）失配相干检测器，依赖于不完美的CSI。对于非相干情况，推导了低至中等信干噪比（SINR）下的精确似然表达式和闭式检测器；对于不完美CSI情况，推导了低至中等SINR下的对应闭式检测器，并通过成对错误概率（PEP）分析性能，得到了基于高斯-切比雪夫求积的易处理近似。数值和解析结果表明，所提出的非相干检测器与最优ML检测器性能接近，验证了其实用性。
英文摘要
Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for improving communication reliability in radar-communication coexistence (RCC) scenarios, particularly for communication users (CUs) located inside radar exclusion zones, where only the radar is permitted to operate and the two systems remain uncoordinated. In such settings, CUs may suffer from strong radar interference whose phase is random and difficult to track while also facing difficulty in obtaining accurate CSI from the base station. These challenges become even more critical in the presence of RIS phase errors, which limit the applicability of conventional coherent detection methods. Motivated by these practical limitations, this paper develops an RIS-assisted RCC framework for a CU operating in an uncoordinated RCC setting. Within this framework, we derive two practical maximum-likelihood (ML)-based detectors, both of which avoid tracking the radar interference phase: 1) a non-coherent detector that does not require instantaneous CSI and incorporates RIS phase uncertainty, and 2) a mismatched coherent detector that relies on imperfect CSI. For the non-coherent case, we derive an exact likelihood expression and a closed-form detector in the low to moderate SINR regime. For the imperfect CSI case, we derive the corresponding closed-form detector in the low-to-moderate SINR regime, and analyze performance through pairwise error probability (PEP), yielding a tractable approximation based on Gauss-Chebyshev quadrature. Numerical and analytical results show that the proposed non-coherent detector closely matches the optimal ML detector, validating its practical usefulness.
