Abstract
The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time.
Highlights
Unmanned Aerial Vehicles (UAVs) are increasingly used in industries such as agriculture, entertainment, logistics, and surveillance due to their high speed, maneuverability, and agility
The results show that use of single weight value WHO in a UAV handover decision (UHD) avoids the conflicting objectives and enables the Policy Optimization (PPO)
The convergence and handover decisions of a UHD further improve with multiple UAVs as more data with diverse characteristics is collected in the trajectory memory in a shorter time
Summary
Unmanned Aerial Vehicles (UAVs) are increasingly used in industries such as agriculture, entertainment, logistics, and surveillance due to their high speed, maneuverability, and agility. The SL mobility model used in this study does not capture the real environment in which a high-degree tilt of beams is required for high-altitude UAVs, which may affect the service of terrestrial UEs. Instead of altering the network to improve the handover performance, a Q-learning-based handover decision scheme (QHD) for UAVs was presented in Chen et al [8] that used RW mobility model with constant UAV speed and altitude. Dynamic optimization of the UAV handover decision through the proposed DRL framework with PPO algorithm determined the moment for UAV handover execution and enabled the UAV to maintain stable communication with high data rates.
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