# UAV Communications for 5G and Beyond: Recent Advances and Future Trends

arXiv: https://arxiv.org/abs/1901.06637 · HTML: https://mlsanity.com/paper/1901.06637/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2019.

## Built on by (most cited first, 25 of 25)

- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2505.08448/) (2025)
- [Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation](https://mlsanity.com/paper/2303.12947/) (2023)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Attention Based Communication and Control for Multi-UAV Path Planning](https://mlsanity.com/paper/2112.12584/) (2021)
- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2411.14052/) (2024)
- [Efficient UAV Trajectory-Planning using Economic Reinforcement Learning](https://mlsanity.com/paper/2103.02676/) (2021)
- [UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations](https://mlsanity.com/paper/2202.10338/) (2022)
- [Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference](https://mlsanity.com/paper/2403.13827/) (2024)
- [Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks](https://mlsanity.com/paper/2410.22578/) (2024)
- [Networking of Internet of UAVs: Challenges and Intelligent Approaches](https://mlsanity.com/paper/2111.07078/) (2021)
- [Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management](https://mlsanity.com/paper/2203.06465/) (2022)
- [DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem](https://mlsanity.com/paper/2408.05712/) (2024)
- [PCA-Featured Transformer for Jamming Detection in 5G UAV Networks](https://mlsanity.com/paper/2412.15312/) (2024)
- [PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling](https://mlsanity.com/paper/2510.06355/) (2025)
- [Performance Comparison of Aerial RIS and STAR-RIS in 3D Wireless Environments](https://mlsanity.com/paper/2512.08755/) (2025)
- [Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment](https://mlsanity.com/paper/2512.20624/) (2025)
- [AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey](https://mlsanity.com/paper/2606.23125/) (2026)
- [PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks](https://mlsanity.com/paper/2607.17922/) (2026)
- [Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach](https://mlsanity.com/paper/2609.30690/) (2026)

## Abstract (arXiv)

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.