Hybrid Smoothing Method (HSM) in Cyclostationary Signal Detection for Cognitive Radio

One of the major challenging issues in wireless communication is spectrum scarcity. In order to better utilize the licensed spectrum, the concept of cognitive radio has been introduced in which unlicensed users (secondary users) sense the spectrum and use the available bandwidth for their own transmission. One of the methods for detecting licensed users is through cyclostationary processing, which is based on the estimation of the spectral correlation function of the received signal. In this paper a new method for the detection of licensed users is proposed. The proposed Hybrid Smoothing Method (HSM) combines pre-existing time smoothing and frequency smoothing algorithms in cyclostationary processing in a cascading format. HSM estimates the SCF of the received signal and then sets a threshold for its decision. The threshold to switch from frequency smoothing to time smoothing in HSM is set by Neyman-Pearson lemma. Simulation results show that HSM not only works in a noisy environment but also outperforms a standalone time or frequency smoothing algorithm in terms of probability of signal detection