Investigation of spectrum sensing under colored noise for cognitive radio network

Spectrum sensing (SS) is an essential requirement in Cognitive Radio (CR) to detect licensed user (Primary user (PU)) and to access the opportunistic spectrum for unlicensed users (secondary users). Several sensing techniques are limited by multipath fading and shadowing which degrade the sensing performance. Hence noise plays an important role in spectrum sensing. Herein, we examine the condition of correlated noise based on eigenvalue technique. The consideration of Standard Condition Number (SCN) based statistics for decision that statistics are further based on Random Matrix Theory (RMT). First we analyze the eigenvalue based Marchenko-Pastur (MP) Law in presence of correlated noise. Due to degradation in performance of MP Law, new SCN based threshold is used. We analyze that the new bound increases the performance in case of correlated noise. Cooperative spectrum sensing based hard decision rule is to analysis the performance of spectrum sensing. Here, AND, OR and Majority rule is analyzed under the condition of noise correlation and also analyzed the effect of correlation on sensing performance using these rules.