基于改进人工蜂群算法的认知无线电频谱分配

认知无线电是一种提高频谱利用率的新技术.认知无线电系统中,动态频谱分配技术在利用闲置频谱资源上发挥关键作用.本文介绍了频谱分配的图论着色模型,提出一种改进的蜂群算法.基本人工蜂群算法存在搜索精度不高和收敛速度较慢的问题,本文改进的人工蜂群算法引入了基于差分进化算法的搜索策略和高斯变异的侦察策略,并且对选择策略进行改进,提高了种群的多样性.将改进的蜂群算法应用于频谱分配模型中,实验结果表明,改进的人工蜂群算法可以得到更好的系统收益,加快了收敛速度.

Spectrum Allocation Based on An Improved Artificial Bee Colony Algorithm in Cognitive Radio System

Frequency spectrum, which is the important resource for wireless communications, may become crowded to accommodate diverse type of applications. The static spectrum allocation results in a contradiction between spectrum constraint and uneven utilization of spectrum. Cognitive radio is an emerging technique to improve the efficiency and flexibility of spectrum utilization. In cognitive radio system, dynamic spectrum allocation is essential to make use of free frequency without interfering licensed users. This paper analyzes a spectrum allocation system based on graph coloring model and proposes a improved artificial bee colony algorithm. The basic artificial bee colony algorithm suffers from the problems of slower convergence and low exploration precision. The improved artificial bee colony1 algorithm introduces a search strategy based on differential evolution and Gaussian mutation detection strategy. In addition, a novel probability choice model is proposed to increase population diversity. The simulations are conducted to apply the improved ABC algorithm into spectrum allocation. The simulation results show that the improved ABC algorithm can achieve better system benefit and accelerate the convergence speed compared with the basic ABC algorithm.