Optimal Biased Association Scheme with Heterogeneous User Distribution in HetNets

Abstract

In this paper we consider the user association problem in heterogeneous networks where each user chooses to be associated to a base station based on the biased downlink received power. In contrast to previous studies where users are usually assumed to be uniformly distributed, and thereby a per-tier SINR biasing factor is used to balance the load of BSs among different tiers, we examine in this paper a scenario that one cell is overloaded, i.e., has a higher user intensity. In this case, the adjustment of the per-tier biasing factor becomes unreasonable, and thus we propose to adjust the biasing factor of the overloaded cell to offload the traffic to its surrounding cells. By maximizing the mean user utility in the area of this overloaded cell and its neighboring cells, the optimal biasing factor can be obtained. It is found that in the scenario where the overloaded cell is fully surrounded by a macro cell, the optimal biasing factor logarithmically decreases with the user's intensity of the overloaded cell. Numerical results demonstrate that by using the optimal biasing factor of the overloaded cell in the considered scenario, the mean user rate in the overloaded cell increases from 23 to 87 % as the average number of cluster users increases from 10 to 80, and the overall mean user rate is also improved compared to the previous biased scheme without the adjustment of the overloaded cell in the literature. Our analysis provides guidance on the optimal tuning of the biasing factor of an overloaded cell and, is a step forward towards the goal of the adjustment of the biasing factor in a per-station fashion under heterogeneous spatial user distribution.