Research On Mobility Load Balance Optimization Algorithm In Dense Network

The dense network is one of the important technologies in 5G network evolution.With the densification of the network,a variety of low-power nodes are deployed in the hot spots,greatly enhance the network capacity,but also has brought great challenges to optimize the network performance.Load balancing in dense heterogeneous networks is an important measure of network performance in network performance optimization.At the same time,small base station densification deployment with in-band wireless backhaul in the dense heterogeneous network can effectively reduce the network deployment costs,increase the flexibility of small base station deployment,is becoming a hot research.The problem of load balancing in dense heterogeneous network with in-band backhaul also requires further study.This paper focuses on the load balancing problem in 5G dense network.The main contents and innovations of the paper are summarized as follows:In order to solve the load imbalance problem caused by uncertainty of traffic in heterogeneous dense cellular networks,this paper proposes a load balance algorithm through small cell range expansion.The proposed algorithm is based on Partially Observable Markov Decision Process(POMDP).By observing the packets of system user during the perceptual cycle,the next cycle system possible load state can be doptd out.Then,the cell range expansion(DCRE)offset value is dynamically adjusted to take action in advance,reaching the purpose of optimizing the system load balance.To solve the problem efficiently,a heuristic algorithm is used to approximate and quickly get the suboptimal solution.Simulation results show that the proposed method can to achieve load balance optimization in dense hetrogeneous network,and improve the system user throughput and resource utilization rate.Aiming at the problem of load imbalance caused by irrational bandwidth allocation in dense homogeneous networks,a self-backhaul aware user access load balancing scheme is proposed.Firstly,a User Access-Load Balancing Strategy(UA-LBS)is proposed based on the load state of each small base station access and backhaul resource in dense heterogeneous network.Secondly,the Q-Learning algorithm is used to allocate wireless access and backhaul bandwidth in each small base station.For different allocation factors,user re-access according to the UA-LBS,and get different system utility,and then get the optimal bandwidth allocation strategy to ensure load balancing while achieving system utility maximization.The simulation results show that the scheme improves the network load balancing in the self-backhaul scene of dense heterogeneous network,and improves the user rate experience.