Energy-Efficient Resource Allocation in Cognitive Radio Networks Using Self-Adaptive Harris Hawks Optimization Algorithm

Cognitive radio network (CRN) is a potential technology to determine resource inadequacy in wireless networks. However, when changes in resource availability in CRN occur, the existing approaches for resource allocation resume optimizing resources without utilizing previous data. To solve this problem, a self-adaptive-Harris Hawks optimization (SA-HHO) algorithm is proposed for resource allocation in CRN efficiently. Initially, in the system model, the power consumption, spectrum channels in CRN, signal interference to noise ratio (SINR), and energy efficiency for resource allocation are estimated. After that, the proposed SA-HHO optimization algorithm is used for resource allocation by updating the position of hawks with a self-adaptive strategy, which prevents the model from falling into local optima. The experimental analysis indicates promising results for energy efficient resource allocation in CRN with interference, throughput and energy efficiency of in base station 10, 15, 20, and 25 are 21.2, 23.0, 24.7, and 20.4 J which is lesser than other existing resource allocation methods like flower pollination optimizer (FPO), modified non-domination sorted genetic algorithm (MNSGA), and block coordinated descent (BCD).