In this paper, we study the problem of promptly detecting the presence of non-cooperative activity from one or more Reconfigurable Intelligent Surfaces (RISs) with unknown characteristics lying in the vicinity of a Multiple-Input Multiple-Output (MIMO) communication system using Orthogonal Frequency-Division Multiplexing (OFDM) transmissions. We first present a novel wideband channel model incorporating RISs as well as non-reconfigurable stationary surfaces, which captures both the effect of the RIS actuation time on the channel in the frequency domain as well as the difference between changing phase configurations during or among transmissions. Considering that RISs may operate under the coordination of a third-party system, and thus, may negatively impact the communication of the intended MIMO OFDM system, we present a novel RIS activity detection framework that is unaware of the distribution of the phase configuration of any of the non-cooperative RISs. In particular, capitalizing on the knowledge of the data distribution at the multi-antenna receiver, we design a novel online change point detection statistic that combines a deep support vector data description model with the scan $B$-test. The presented numerical investigations demonstrate the improved detection accuracy as well as decreased computational complexity of the proposed RIS detection approach over existing change point detection schemes.

翻译：本文研究了在多输入多输出正交频分复用通信系统附近存在特性未知的一个或多个可重构智能表面时，如何及时检测其非合作活动的问题。我们首先提出了一种包含可重构智能表面及非可重构静态表面的新型宽带信道模型，该模型同时捕捉了频域中RIS驱动时间对信道的影响，以及传输期间或传输间相位配置变化带来的差异。考虑到可重构智能表面可能在第三方系统协调下运行，从而对目标MIMO OFDM系统的通信产生负面影响，我们提出了一种新型的RIS活动检测框架，该框架无需知晓任何非合作RIS相位配置的分布特性。具体而言，通过利用多天线接收端数据分布的先验知识，我们设计了一种结合深度支持向量数据描述模型与扫描$B$检验的新型在线变点检测统计量。数值实验表明，与现有变点检测方案相比，所提出的RIS检测方法在提升检测精度的同时显著降低了计算复杂度。