Detection for Spectrum Sensing in CRN under Impact of Distributional Uncertainty of Noise and Interference

Since the advent of Cognitive Radio and exploitation of unused spectrum by secondary devices, spectrum has gained immense importance. In cognitive radio network setting, due to very low received signal to noise ratio (SNR), spectrum sensing has been a challenging task. Further, signal detection is obscured due to presence of accumulated interference for which the statistical distribution is unknown and has significant distributional uncertainty. Covariance based signal detection methods have been proposed by various authors and proved to be better than other methods due to its independence on SNR, accuracy, and medium algorithmic complexity. Some results from Random Matrix Theory (RMT) on modelling of covariance matrices can be used in signal detection. This paper presents an algorithm for detection of Chi-Square distributed independent random signals using Wishart matrix which model the covariance matrix. A centering matrix is employed to scale the sample covariance matrix with Wishart distribution. The signals under various fading environment are used to evaluate the performance of the proposed algorithm. Also, a DVBT-2K signal is used to evaluate the performance for more realistic conditions. Furthermore, quantification of distributional uncertainty of accumulated interference in DTV based CRN is calculated using differential entropy of pdf of the interference. The quantified uncertainty is used to calculate the new threshold for detection. As per our knowledge, it is observed in the proposed work that the Maximum-Minimum Eigenvalue (MME) based proposed algorithm performs better than other proposed schemes in terms of probability of detection and complexity.