Autocorrelation-Based Spectrum Sensing Algorithms for Cognitive Radios

Cognitive radio is an enabling technology for opportunistic spectrum access. Spectrum sensing is a key feature of a cognitive radio whereby a secondary user can identify and utilize the spectrum that remains unused by the licensed (primary) users. Among the recently proposed algorithms the covariance-based method of [1] is a constant false alarm rate (CFAR) detector with a fairly low computational complexity. The low computational complexity reduces the detection time and improves the radio agility. In this paper, we present a framework to analyze the performance of this covariance-based method. We also propose a new spectrum sensing technique based on the sample autocorrelation of the received signal. The performance of this algorithm is also evaluated through analysis and simulation. The results obtained from simulation and analysis are very close and verify the accuracy of the approximation assumptions in our analysis. Furthermore, our results show that our proposed algorithm outperforms the algorithm in [1].