Enhanced Cooperative Spectrum Sensing in Cognitive Radio Network Using Flower Pollination Algorithm

Spectrum sensing is of great importance in Cognitive Radio Network (CRN). It cannot be achieved by a single user owing to multipath fading and shadowing effect. Therefore, more than one user is required to accurately sense the spectrum availability as in Cooperative Spectrum Sensing. All sensing statistics are collected at Fusion Center (FC) from cooperative users and FC combines them to reach to a common global decision. As these users are far apart from each other, therefore they experience different channel conditions. So, it is necessary to deal with the incoming data received from these Secondary Users (SU) differently. The proposed scheme of Flower Pollination Algorithm (FPA) intelligently finds optimum weighting coefficients against cooperative users' information and utilizes these weights in the global decision of the Soft Decision Fusion (SDF). This scheme is able to find optimum weights that lead to minimum false alarm, high detection and minimum error probability. The system is simulated for different numbers of the cooperative users and Signal-to-Noise Ratios (SNRs) that shows better sensing performance of the proposed FPA based scheme against the Differential Evolution (DE), Genetic Algorithm (GA), Maximum Gain Combination (MGC), Particle Swarm Optimization (PSO), and Count techniques.