α-Fair Mobility Management in 5G Networks

Abstract

Mobility management in 5G is challenging due to the usage of high frequencies and dense cell deployments. As a result, users experience frequent handovers that cause an interruption in transmission/reception and diminish network capacity. In the common handover algorithm, the target Base Station (BS) is selected based solely on the signal strength, while the available resources are not considered, leading to overloaded cells, especially for macro cells with large coverage. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to provide $\alpha $ -fairness in data rates among users and to reduce handovers. To accomplish that, we jointly perform user assignment and resource allocation while accounting for the interruption due to handovers. This is an integer nonlinear program and, by relaxing it, an upper bound is obtained. Further, because of the time complexity of the original problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm, which finds near-optimal user-to-BS assignments and the amount of resources that should be allocated to a user. Our approach outperforms considerably state of the art in terms of fairness and handover rate while being within at most 12% of the optimum in most cases.