State Aware-Based Prioritized Experience Replay for Handover Decision in 5G Ultradense Networks

Abstract

The traditional handover decision methods depend on the handover threshold and measurement reports, which cannot efficiently resolve the frequent handover issue and ping-pong effect in 5G (5 generation) ultradense networks. To reduce the unnecessary handover and improve the QoS (quality of service), combine with the analysis of dwell time, we propose a state aware-based prioritized experience replay (SA-PER) handover decision method. First, the cell dwell time is computed by the geometrical analysis of real-time locations of mobile users in cellular networks. The constructed state aware sequence including SINR, load coefficient, and dwell time is normalized by max-min normalization method. Then, the handover decision problem in 5G ultradense networks is formalized as a discrete Markov decision process (MDP). The random sampling and small batch sampling affect the performance of deep reinforcement learning methods. We adopt the prioritized experience replay (PER) method to resolve the learning efficiency problems. The state space, action space, and reward functions are designed. The normalized state aware decision matrix inputs the DDQN (double deep Q-network) method. The competitive and collaborative relationships between vertical handover and horizontal handover in 5G ultradense networks are mainly discussed. And the high average network throughput and long average cell dwell time make sure of the communication quality for mobile users.