Discrete Hughes-Hartogs optimized dual vector handoff for spectrum management in cognitive radio networks

Cognitive radio network (CRN) has occupied rich awareness on amazing potentiality of spectrum utilization for the secondary users. In CRN, effective spectrum management has received more attention by increasing number of communication devices and several techniques have been evolved in recent years. However, efficient spectrum sensing with minimum delay reducing number of spectrum handoffs and maximum throughputs are some of the major challenges to be addressed. In this proposed work, discrete Hughes-Hartogs optimal and dual vector spectrum management (DHHO-DVSM) in CRN are used for effective utilization of channel selection. The expected maximization Q channel selection is designed to obtain an efficient idle channels for improved transmission of the secondary user. The optimal channel is attained by the number of idle channels maintained from the Q channel selection list. Based on each SU requests, discrete Hughes-Hartogs particle swarm optimal channel selection is proposed to find the best channel by computing the fitness function for discrete factor. Finally, dual constraints-based vector spectrum handoff model is proposed to produce the hyper plane based on the probability conditional factors with the objective of minimizing the number of handoffs for secondary users. The simulation result shows that the proposed method has transparent edges over conventional channel selection models. In addition, results are verified to learn the optimal channel selection with minimal sensing delay, increasing throughput and minimal handoffs. The proposed algorithm DHHO-DVSM is 22.8% comparatively better than E-CRNs and 41% improvement than SpecPSO.

Automated summary

The study focuses on effective spectrum management in CRN, addressing challenges such as spectrum sensing, reducing handoff frequency, and increasing throughput. The proposed DHHO-DVSM algorithm optimizes channel selection and handoff models to improve transmission efficiency, with a 22.8% improvement over E-CRNs and a 41% improvement over SpecPSO.