密集网络下基于Self-Backhaul感知的用户接入负载均衡算法

针对密集异构网络自回程场景中带宽分配不合理引起的负载不均衡问题,提出一种基于self-backhaul感知的用户接入负载均衡方案.首先根据密集异构网络下各个小基站接入与回程资源的负载状态提出一种用户接入负载均衡策略;其次利用Q-Learning算法对各个小基站带内无线接入与回程带宽分配进行学习,用户在不同带宽分配因子下,根据用户接入负载均衡策略进行重新接入,得到不同接入情况下的系统效用,进而得到最优带宽分配策略,保证负载均衡性的同时实现系统效用最大化.仿真结果表明,该方案在密集异构网络自回程场景中提高了网络负载均衡性,同时提升了用户速率体验.

User Association Load Balancing Algorithm Based on Self-Backhaul Aware in Dense Networks

In order to solve the problem of load imbalance caused by irrational bandwidth allocation in dense heterogeneous networks,a self-backhaul aware user access load balancing scheme was proposed.Firstly,a user association-load balancing strategy (UA-LBS) was described based on the load state of each small base station access and backhaul resource in dense heterogeneous network.Secondly,the QLearning algorithm was used to allocate wireless access and backhaul bandwidth in each small base station.For different allocation factors,it can ensure user to re-access according to the UA-LBS to get different system utility,and then to get optimal bandwidth allocation strategy to ensure load balancing while achieving system utility maximization.Simulation shows that the scheme improves the network load balancing in the self-backhaul scene of dense heterogeneous network,and improves the user rate experience.