基于改进遗传算法的C-RAN网络动态无线资源分配

Dynamic Wireless Resource Allocation Based on Improved Genetic Algorithm in C-RAN Networks

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究。首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比。实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17%。

For the inefficiency problem of traditional static resource allocation in cloud radio access network(C-RAN)and the lack of resource type in dynamic wireless resource allocation,a dynamic wireless resource allocation scheme based on the quality of service(QoS)constraint is proposed.Radio resources are studied from three dimensions:remote radio head(RRH)selection,subcarrier allocation and power allocation.Firstly,according to the transmission model in traditional C-RAN and QoS constraint,an optimization problem with the variable of transmission power and the goal of throughput maximization is established.Then,the original optimization scheme is transformed to improve the system throughput by optimizing the RRH selection,subcarrier allocation and power allocation based on the improved genetic algorithm.Finally,the time complexity of the improved genetic algorithm is compared with other intelligent algorithms under the change of population size.The experimental results show that the proposed algorithm has low time complexity and the average throughput gain of the proposed resource allocation scheme is 17%.