Eigenvalue Based Detection by Combining Eigenvector‐Correlated Signal in Low SNR Environment

Eigenvalue detection is extensively utilized in numerous applications, including spectrum sensing in cognitive radio. However, the characteristics of the Tracy–Widom () distribution and its lack of accurate close‐form expression limit the detection performance based on the extreme eigenvalue. The paper introduces an eigenvector correlation (EVC) based detection. It extracts the eigenvalue of a weak component from the Marchenko–Pastur law (MP‐law) bulk by combining a priori known eigenvector‐correlated signal. The extracted eigenvalue then follows an easily analyzable and tractable Gaussian distribution according to the random matrix theory. The proposed method significantly improves sensing performance, which is theoretically analyzed and compared with traditional maximum eigenvalue‐based detection (MED) and trace‐based sensing. In addition, a cumulative EVC (CUM–EVC) is further developed for the multiple eigen‐component signal. Numerical results are presented to validate the theoretical analysis and demonstrate the reliability of the proposed detectors.