# Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

arXiv: https://arxiv.org/abs/2001.07937 · HTML: https://mlsanity.com/paper/2001.07937/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management](https://mlsanity.com/paper/2412.16167/) (2024)
- [Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach](https://mlsanity.com/paper/2404.08113/) (2024)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization](https://mlsanity.com/paper/2504.18371/) (2025)

## Abstract (arXiv)

Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve H-RRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions in different drone's altitudes/speeds have been presented, which promote a revision of the legacy handover schemes and redefining the boundaries of cells in the sky.