Mobility Management for Cellular-Connected UAVs: Model-Based Versus Learning-Based Approaches for Service Availability

Mobility management for terrestrial users is mostly concerned with avoiding radio link failure for the edge users where the cell boundaries are defined. The problem becomes interesting for an aerial user experiencing fragmented coverage in the sky and line-of-sight conditions with multiple ground base stations (BSs). For aerial users, mobility management is not only concerned with avoiding link failures but also avoiding unnecessary handovers while maintaining extended service availability, especially in up-link communication. The line of sight conditions from an Unmanned Aerial Vehicle (UAV) to multiple neighboring BSs make it more prone to frequent handovers, leading to control packet overheads and delays in the communication service. Depending on the use cases, UAVs require a certain level of service availability, which makes their mobility management a critical task. The current mobility robustness optimization (MRO) procedure that adaptively manages handover parameters to avoid unnecessary handovers is optimized only for terrestrial users. It needs to be updated to capture the unique mobility challenges of aerial users. In this work, we propose two approaches to accomplish this: 1) A model based service availability-aware MRO where handover control parameters, such as handover margin and time to trigger are tuned to maintain high service availability with a minimum number of handovers, and, 2) A deep Q-network based model free approach for decreasing unnecessary handovers while maintaining high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40% while the number of handovers is reduced by more than 50% as compared to traditional approaches.