Abstract: 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 Q-Learning 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.

Key words: dense network, load balance, self-backhaul, Q-learning