# A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

arXiv: https://arxiv.org/abs/2010.09317 · HTML: https://mlsanity.com/paper/2010.09317/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)
- [LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2305.15858/) (2023)
- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms](https://mlsanity.com/paper/2401.11118/) (2024)
- [Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks](https://mlsanity.com/paper/2407.10382/) (2024)
- [UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2502.07388/) (2025)
- [Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO](https://mlsanity.com/paper/2406.16934/) (2024)
- [UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks](https://mlsanity.com/paper/2205.03335/) (2022)
- [VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT](https://mlsanity.com/paper/2210.08132/) (2022)
- [Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design](https://mlsanity.com/paper/2409.01411/) (2024)
- [Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRL](https://mlsanity.com/paper/2504.02688/) (2025)
- [Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network](https://mlsanity.com/paper/2506.12831/) (2025)
- [Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2507.14299/) (2025)
- [Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.23905/) (2025)
- [Distributed Online Submodular Maximization under Communication Delays: A Simultaneous Decision-Making Approach](https://mlsanity.com/paper/2603.27803/) (2026)
- [Arm2Air: Cross-Embodiment Skeleton Transfer for 3D Relay Formation](https://mlsanity.com/paper/2607.27627/) (2026)

## Abstract (arXiv)

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.