Dual-Connectivity Handover Scheme for a 5G-Enabled Ambulance

Abstract

Remote first-aid treatment on ambulances is a promising application of 5G. However, there still exist gaps between the capabilities of current 5G networks and the stringent requirements of remote emergency on ambulances. Dual connectivity (DC) is an efficient technology to fill these gaps by integrating 5G millimeter wave (mmWave) with Sub-6GHz networks. In this paper, we investigate a dual-connectivity handover scheme to enhance the transmission rate of the wireless links for a 5G-enabled ambulance. Due to the long delay caused by signal transmission and processing, the conventional handover schemes based on reference signal received power (RSRP) measured by users are not sufficiently sensitive to the rapidly changing propagation environments surrounding the 5G-enabled ambulance. Instead, considering the randomness of environments and the delay caused by the handover process, we employ a deep Q network (DQN)-based algorithm to find a far-sighted policy for solving the handover problem. However, due to the drawbacks of single-step bootstrapping, value overestimation, and low-efficiency exploration, the vanilla DQN is performance-limited. To this end, we adopt effective techniques including multi-step learning, double DQN, and NoisyNet to improve learning performances, and propose a noisy double DQN (NDDQN)-based dual-connectivity handover scheme. Simulation results verify the effectiveness and superiority of our NDDQN-based handover scheme compared with the vanilla DQN and upper confidence bound (UCB)-based handover schemes, and then show that our handover scheme can adapt to various handover models.