Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading
arXiv (Cornell University)(2023)
摘要
Robust spectrum sensing is crucial for facilitating opportunistic spectrum
utilization for secondary users (SU) in the absense of primary users (PU).
However, propagation environment factors such as multi-path fading, shadowing,
and lack of line of sight (LoS) often adversely affect detection performance.
To deal with these issues, this paper focuses on utilizing reconfigurable
intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein
both the multi-path fading and noise are correlated. In particular, to leverage
the spatially correlated fading, we propose to use maximum eigenvalue detection
(MED) for spectrum sensing. We first derive exact distributions of test
statistics, i.e., the largest eigenvalue of the sample covariance matrix,
observed under the null and signal present hypothesis. Next, utilizing these
results, we present the exact closed-form expressions for the false alarm and
detection probabilities. In addition, we also optimally configure the phase
shift matrix of RIS such that the mean of the test statistics is maximized,
thus improving the detection performance. Our numerical analysis demonstrates
that the MED's receiving operating characteristic (ROC) curve improves with
increased RIS elements, SNR, and the utilization of statistically optimal
configured RIS.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要