Statistical Tests for Signal Detection Using Random Matrix Theory

Signal detection is a fundamental problem in cognitive radio. Energy detection is optimal for detecting independent and identically distributed (iid) signals, but not optimal for detecting the correlated signals. This paper introduces a unified framework for the signal detection where the noise variance and the channel between the source and sensor are unknown at the receiver. The statistical tests are proposed based on the sample covariance matrix calculated from the received signal samples. Using the recent results from random matrix theory, a practical way to evaluate the threshold for the tests is provided. Statistical tests do not need any information of the signal, the channel and noise power as a priori. The Performance of the statistical tests are compared with energy detection (ED) algorithm and covariance absolute value (CAV) method through simulation analysis. Simulations based on voice recorded signals are presented to verify the proposed statistical tests.