Enabling Proportionally Fair Mobility Management in 5G Networks

Abstract

Mobility management in 5G, especially at higher frequencies, is challenging because the signal quality fluctuates significantly due to blockages of Line of Sight (LoS), shadowing and user mobility. As a result, users experience frequent handovers, which reduce the network capacity. In order to perform smooth network operation, the decisions when to handover and to which Base Station (BS) a user is to be assigned should be considered jointly. Another important goal is to strive for fairness in data rates among the users. To this end, in this paper, we formulate an optimization problem whose solution provides proportional fairness and reduces the handover rate significantly. To solve the problem, we propose a Deep Reinforcement Learning (DRL) algorithm, specifically a Deep Q Network (DQN), which turns out to find a near-optimal user-to-BS assignment. We compare our approach with other state-of-the-art baselines and show that it outperforms them considerably in terms of fairness, handover, ping-pong and radio link failure rates while being within 96% of the optimal solution. Our DQN algorithm also reduces the handover rate by 86% and avoids ping-pong handovers.