Proactive Handover Management in 6G Low-Altitude Economy Networks for Aerial Vehicles Using Artificial General Intelligence

The rise of unmanned aerial vehicles (UAVs) and electric vertical take-off and landing (eVTOL) aircraft is accelerating the growth of the low-altitude economy (LAE), enabling mobility beyond conventional ground-based transport. However, aerial vehicles or flying vehicles (FVs) in LAE environments face significant communication challenges due to high mobility, frequent handovers between base stations (BSs), and the susceptibility of mmWave bands to blockage and path loss. Reactive handover mechanisms—triggered only after link degradation—often lead to disconnections and degraded service quality, particularly in dense urban areas. Moreover, uneven FV distribution can cause BS load imbalances, further compromising quality of service (QoS). To address these challenges, we propose a proactive BS association framework for intelligent handover management using artificial general intelligence (AGI). Our approach leverages deep learning to jointly predict future received signal strength indicator (RSSI) and BS load, enabling an autonomous decision algorithm to select optimal BSs for stable, high-throughput connectivity while minimizing unnecessary handovers. Simulation results demonstrate that the proposed joint prediction-based strategy significantly reduces handover frequency and improves average throughput compared to reactive, single-metric base-lines and two additional benchmark predictors introduced for extended evaluation. These findings underscore the potential of predictive, AGI-driven mobility management to enhance the stability and performance of communication networks in the emerging LAE ecosystem.