Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network

Abstract

The Cognitive Radio Network (CRN) in 5G heterogeneous network has trade-off between energy efficiency and spectrum sensing efficiency. Energy Efficiency is important for designing the battery-powered cognitive radio network. Existing methodologies are mainly focused on solving the optimization problem of energy efficiency in spectrum sensing using convex optimization. However, The real-time spectrum sensing is a non-convex optimization problem. In this paper, energy efficiency for various spectrum sensing scenarios is modeled as non-convex optimization problem. To detect spectrum holes with improved energy utilization, this paper proposes a novel hybridization of Particle Swarm Optimization (PSO) with Gravitational Search Algorithm (GSA) called as Hybrid PSO-GSA. With the proposed novel hybridization of PSO and GSA, it is possible to achieve balanced trade-off between exploration and exploitation abilities of PSO-GSA algorithm. In addition to that, with the incorporation of mutation and crossover factor in the PSO-GSA, the proposed algorithm is efficiently able to detect the spectrum holes with the optimized values of transmission power, sensing bandwidth and power spectral density. Thus, improving the energy efficiency of the spectrum sensing. Simulation results substantiate the efficiency of PSO-GSA in optimizing the energy efficiency for spectrum sensing in terms of transmission power, spectrum sensing bandwidth and power spectral density compared with existing PSO and Artificial Bee Colony (ABC) algorithms.