Cooperative Spectrum Sensing Optimization in Cognitive Radio networks based on a Hybrid (MFO-GDO) Heuristic Search Algorithm

Cognitive radio Network (CRN) is an intelligent technology and it periodically monitor unused licensed spectrum in a specific frequency band. The main issues with spectrum sensing in CRNs are the hidden terminal problem, which occurs during cognitive radio shading, severe multi-path faded or in buildings with high infiltration loss, while operating near a primary user (PU). Due to the hidden terminal problem, a cognitive radio (CR) can have failed to notice the PU's presence. Then access the unlicensed channel, cause interference in the license scheme, while this interference occurs in the system the probability errors will occurs in the network and reduces the spectrum utility. To overcome these issues, Quick Cooperative Spectrum Sensing (CSS) optimization framework in CRN (CSS-CRN) based on a May Fly optimization (MFO) and Gradient Descent Optimization (GDO) algorithm is proposed in this paper. Here, the weight vectors of CSS-CRN are optimized utilizing the hybrid heuristic Search based optimization algorithm namely May Fly optimization (MFO) and Gradient Descent Optimization (GDO) algorithm. Finally these weight vectors are used in the data fusion centre to assign spectrum in secondary users (SUs).