AI 中文总结
研究在相关噪声条件下，基于广义似然比检验（GLRT）和能量检测器（ED）框架的可重构智能表面辅助频谱感知。推导相关参数估计构建检验统计量，确定最优RIS相位偏移矩阵，得出GLRT相比ED有更高检测概率的结论。
AI 中文摘要
频谱感知对于实现认知无线电网络至关重要，其中次要用户需要检测主要用户的存在以利用频谱。然而，检测能力受多径衰落、相关噪声、主要用户传输功率等未知传播环境因素影响。本文在相关噪声条件下，使用广义似然比检验（GLRT）和能量检测器（ED）框架研究可重构智能表面辅助的频谱感知。首先推导未知信道状态和发射功率的最大似然估计，并利用这些估计构建基于GLRT的检验统计量。通过最优配置RIS相位偏移矩阵最大化估计信道增益，还推导了最优配置RIS时ED的检测和虚警概率。数值接收机输出特性表明，所提GLRT相比ED具有更高检测概率，尤其在相关噪声和观测次数有限的情况下。
英文摘要
Spectrum sensing (SS) is crucial for realising cognitive radio networks, where the secondary user (SU) needs to detect the presence of a primary user (PU) in order to utilise the spectrum. However, the ability of detection is influenced by unknown propagation environment factors such as multipath fading, correlated noise, transmission power of PU, etc. This paper investigates reconfigurable intelligent surfaces (RIS)-aided SS under correlated noise conditions using a generalised likelihood ratio test (GLRT) and energy detector (ED) frameworks. We first derive maximum likelihood estimates of the unknown channel state and transmit power, and employ these estimates to construct the GLRT-based test statistic using the signal received with an optimally configured RIS. The RIS phase shift matrix is optimally determined to maximise the gain of the estimated channel. Besides, the detection and false alarm probabilities of ED with optimally configured RIS are also derived. The numerical receiver output characteristics (ROC) demonstrate that the proposed GLRT achieves superior detection probability compared to ED, particularly under correlated noise and limited number of observations.
CommentsSubmitted to GLOBECOMM 2026 (Under Review)
