Unified Analysis of Semi-Blind Spectrum Sensing Techniques Under Low-SNR for CRNWs

Spectrum sensing (signal detection) under low signal to noise ratio is a fundamental problem in cognitive radio networks. In this paper, we have analyzed maximum eigenvalue detection (MED) and energy detection (ED) techniques known as semi-blind spectrum sensing techniques. Simulations are performed by using independent and identically distributed (iid) signals to verify the results. Maximum eigenvalue detection algorithm exploits correlation in received signal samples and hence, performs same as energy detection algorithm under high signal to noise ratio. Energy detection performs well under low signal to noise ratio for iid signals and its performance reaches maximum eigenvalue detection under high signal to noise ratio. Both algorithms don't need any prior knowledge of primary user signal for detection and hence can be used in various applications.