Reinforcement learning based predictive handover for pedestrian-aware mmWave networks

This paper discusses the optimal decision-making for predictive handover in millimeter-wave (mmWave) communication networks using information of pedestrian movement. In mmWave communication networks, human blockage causes significant performance degradation. Hence, to maximize the throughput, it might be important to perform a handover predictively using information such as location and velocity of a pedestrian. To optimize the timing to perform the predictive handover, this paper presents a reinforcement learning framework. The important point in this framework is learning the optimal handover policy maximizing the future throughput expected under the locations and velocities of a pedestrian. To learn the optimal policy, this paper applies Q-learning. The numerical results demonstrate that the learned handover decisions outperform the heuristic handover decisions in terms of throughput performance.