Cooperative Spectrum Sensing in Cognitive Radio Networks over Correlated Log-Normal Shadowing

A fundamental component to enable Dynamic Spectrum Access (DSA) capabilities in the future generation of wireless communication systems is the design of robust and efficient spectrum sensing methods to detect licensee users transmitting in a given frequency band. In this context, collaborative methods have been proposed to improve spectrum sensing capabilities over wireless channels, by counteracting shadow-fading phenomena via distributed diversity. However, recent results have shown that the expected benefits of cooperation, which are obtained at the expenses of increased traffic overhead and the need for a control channel, may be significantly hampered when the signals sensed by the cooperative users experience correlated shadow- fading. Moreover, it has been argued that spatial correlation of shadow-fading yields fundamental limits to the performance of cooperative spectrum sensing methods, and practical implications in terms of protocol design and network deployment. Motivated by the above considerations, in this paper we propose an analytical framework for the analysis and design of cooperative spectrum sensing methods over correlated shadow- fading environments, when each cooperative user is equipped with a simple energy-based detector. We will show that the framework requires efficient and accurate methods for modeling the power-sum of correlated Log-Normal Random Variables (RVs), which well describe shadowing phenomena. In particular, three main technical contributions can be found in the present paper: i) we generalize some recently proposed methods to approximate the power-sum of independent Log-Normal RVs to correlated scenarios, ii) we analyze the accuracy of several approximation techniques to compute detection probability over correlated Log-Normal shadowing, which is an unexplored field so far, and iii) we rely on the proposed framework to study the detrimental effect of correlation on the performance of cooperative spectrum sensing methods. Furtherm- ore, numerical results will be shown to validate the proposed framework.