Modified Artificial Bee Colony with firefly algorithm based spectrum handoff in cognitive radio network

Abstract

The metaheuristic algorithm such as Artificial Bee Colony (ABC) and Firefly Algorithm (FA) is a nature and biological inspired for solving the optimization problem. The cognitive radio network is an intellectual network which adapts its performance and operations to provide a perfect result towards its environment. The spectrum sensing and spectrum handoff plays a major role in cognitive radio network. Spectrum sensing is more important for identifying the underutilized spectrum during the spectrum handoff. While progressing the channel, there occurs a premature convergence and local minima problem. The Secondary User (SU) is examining for the white space in the spectrum to produce a smooth transmission, but the channel should not be utilized by the Primary User (PU). When the channel is essential for primary user, the secondary user has to vacate and recognize a new free channel for fast transmission which is referred as spectrum handoff. In this work, Modified Artificial Bee Colony with Firefly Algorithm (MABCFA) is proposed for cognitive radio network to overcome the above challenges. So this proposed work mainly focuses on sensing the best optimal channel for the secondary user and to moderate the delay of spectrum handoff in wireless network. Some of the benchmark functions are used to test the performance of this proposed algorithm. Experimental results show that the performance of MABCFA is 23.81% more robust and effective than the other metaheuristic algorithm.