基于改进樽海鞘群算法的认知异构蜂窝网络资源分配

Cognitive Heterogeneous Cellular Network Resource Allocation Based on Improved Salp Swarm Algorithm

针对认知异构蜂窝网络的上行资源分配问题,提出了基于带宽和功率约束的资源分配算法,并使用改进的群智能算法求解.根据认知无线电技术特性推导出认知家庭用户的带宽和功率分配取值范围,在满足用户服务质量(Quality of Services,QoS)的前提下将更多的资源分配给其他用户,以提升网络中用户的传输需求和缓解网络上行接入负载的压力.针对樽海鞘群算法存在收敛精度低、收敛慢等缺陷,将疯狂算子和动态精英学习因子分别引入领导者和跟随者中,以提升算法寻优效率和寻优精度.将改进的樽海鞘群算法求解基于带宽和功率约束的资源分配算法.仿真实验表明,引入带宽和功率约束的资源分配算法能有效提升网络性能,且在保证用户QoS条件下,能有效提升系统效益和用户接入公平性.

For the uplink resource allocation problem of cognitive heterogeneous cellular networks,a resource allocation algorithm based on bandwidth and power constraints is proposed and solved using an improved swarm intel-ligence algorithm.Based on the characteristics of cognitive radio technology,a range of bandwidth and power alloca-tion values for cognitive home users are derived,and more resources are allocated to other users under the guarantee of satisfying user quality of services(Quality of Services,QoS)to enhance the transmission demand of users in the network and relieve the uplink access load of the network.To address the shortcomings of the bottleneck swarm algo-rithm such as low convergence accuracy and slow convergence,the crazy operator and dynamic elite learning factor are introduced into the leader and follower,respectively,to improve the algorithm's optimality-seeking efficiency and optimality-seeking accuracy.The improved Salp swarm algorithm is solved for the resource allocation algorithm based on bandwidth and power constraints.Simulation experiments show that the resource allocation algorithm with the introduction of bandwidth and power constraints can be effective in improving network performance,and it can effectively improve system efficiency and user access fairness under the condition of ensuring user QoS.