Mobility Management in Low Altitude Heterogeneous Networks Using Reinforcement Learning Algorithm

Abstract

Unmanned aerial vehicle (UAV) base station has been proposed as a promising solution in emergency communication and supplementary communication for terrestrial networks due to its flexible layout and good mobility support. However, the dense deployment of UAV base station and ground base station brings great challenges in the configuration of neighbor cell list (NCL) during handover process. This paper presents a Cascading Bandits based Mobility Management (CBMM) algorithm for NCL configuration in the low altitude heterogeneous networks, where online learning is used to exploiting the historical handover information. In addition to the received signal strength, the cell load of each base station is also considered in the handover procedure. We aim at optimizing the configuration of NCL, so as to improve handover performance by increasing the probability of selecting the best target base station while at the same time reducing the selection delay. It is proved that the signaling overhead can be effectively reduced, since the proposed CBMM algorithm can significantly cut down the number of candidate base stations in NCL. Moreover, by ranking the candidate base stations according to their historical performance, the number of measured base stations in handover preparation phase can be effectively reduced to avoid extra delay. The simulation results of the proposed algorithm and other two existing solutions are presented to illustrate that the CBMM algorithm can achieve efficient handover management.