Comparison of linear and polynomial classifiers for co-operative cognitive radio networks

Cognitive radio (CR) is a promising technology for improving the utilization of the scarce radio spectrum by allowing secondary users to regularly sense the spectrum and opportunistically access the under-utilized frequency bands. However, spectrum sensing in CR environment is a challenging task due to varying radio channel conditions and might lead to interference with licensed users. In this paper, we propose a new framework for CR spectrum sensing based on linear and polynomial classifiers. A cooperative CR network is considered in this paper with CR nodes collaborating in making the decision about spectrum availability. Simulation results indicate that both polynomial and linear classifiers provide high detection rate of primary users with a constant false alarm rate at very small signal to noise ratio conditions. For instance, the proposed techniques can achieve above 90% detection probability at E_b/N₀ = -7dB with observation window of 50 bits and 10% false alarm rate. It is also shown that the performance improves as we increase the sensing time for both schemes.