Spectrum allocation in cognitive radio networks using firefly algorithm

Abstract

In cognitive radio network, Spectrum Allocation (SA) problem is a NP-hard problem which needs to be solved in real time. In this work, a recent bio-inspired heuristic Firefly algorithm (FA) is used for solving SA problem. Three objective functions namely (a) Max-Sum-Reward (MSR), (b) Max-Min-Reward (MMR) and (c) Max-Proportional-Fair (MPF) are optimized to maximize the network capacity. The performance of FA is compared with Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms in terms of quality of solution (network capacity) and timing complexity. The simulation result reveals that the Firefly algorithm improved quality of solution and timing complexity by 17% and 100% respectively compared to PSO, in contrast to 13% and 103% compared to ABC algorithm. FA proved to give maximum utilization of network capacity by assigning conflict free channels to secondary users. © 2013 Springer International Publishing.