Statistical Test based Comparison of Spectrum Sensing Techniques in Cognitive Radio Network

Cognitive radio is an intelligent wireless technology that increases the spectrum efficiency for its usage. CR enriches wireless technology by utilizing the spectrum holes in order to provide high order quality service to users and to minimize the interference that occurs in the network. In the proposed work, two Spectrum Sensing techniques for Cognitive radio network are used which include Cyclostationary detection and Energy detection techniques. The detection of Cyclostationary signal is not a new term but there is a lot of work to be done in this field. In this paper, the parameter used for Cyclostationary signal is Spectral Correlation function. The detection capability of SCF with different windows is used to check the periodicity of the signal using different windows. Due to the periodicity of the baseband signal, SCF would be able to detect the primary user signal at very low SNR. We also analyze in our work that capability of periodicity of the signal of SCF is not only limited to noise affected signal, perhaps it is also able to detect the attenuated signal. We also simulated Energy detector over MIMO fading channel as it models both Rician fading channel and Rayleigh fading channel. The performance is analyzed in terms of Bit error rate by providing low probability of false alarm and high probability of detection. The Statistical test based comparison is made between the two sensing techniques to evaluate the performance in terms of signal to noise ratio. An extensive set of simulations have been conducted in MATLAB in the proposed work.