Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions by integrating computational intelligence into UAV networks. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing.
This paper reviews AI-enabled routing protocols designed primarily for aerial networks, including topology-predictive and self-adaptive learning-based routing algorithms, with an emphasis on accommodating highly-dynamic network topology. To this end, we justify the importance and adaptation of AI into UAV network communications. We also address, with an AI emphasis, the closely related topics of mobility and networking models for UAV networks, simulation tools and public datasets, and relations to UAV swarming, which serve to choose the right algorithm for each scenario. We conclude by presenting future trends, and the remaining challenges in AI-based UAV networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.
Traffic-Adaptive Per-Hop Multipath Routing in Multi-Hop UAV Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；China Mobile Zijin (Jiangsu) Innovation Research Institute（中国移动紫金（江苏）创新研究院）；Queen Mary University of London（伦敦大学玛丽女王学院）；China Telecom Beijing Research Institute（中国电信北京研究院）；Chongqing University（重庆大学）
SDN-Blockchain Based Security Routing for UAV Communication via Reinforcement Learning
The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学）；The State Key Laboratory of CNS/ATM（民航数据通信关键实验室）；School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学）
Pipe Routing with Topology Control for UAV Networks
Computational Science Research Center, San Diego State University（圣地亚哥州立大学计算科学研究中心）；University of California, Irvine（加州大学欧文分校）；Electrical & Computer Engineering Department, San Diego State University（圣地亚哥州立大学电气与计算机工程系）；School of Information & Computer Science, University of California, Irvine（加州大学欧文分校信息与计算机科学学院）
Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Xidian University（西安电子科技大学）；China Academy of Railway Sciences Corporation Limited（中国铁道科学研究院集团有限公司）
TRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs
Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）；School of Automation Science and Electrical Engineering at Beihang University（北京航空航天大学自动化科学与电气工程学院）；State Key Laboratory of Intelligent Manufacturing Systems Technology（智能制造系统技术国家重点实验室）；School of Transportation, Southeast University（东南大学交通学院）；Digital Health Research Center, Haihe Lab of ITAI（海河信息技术与人工智能实验室数字健康研究中心）；School of Cyber Science, Nankai University（南开大学网络空间科学学院）；China-SCO Digital Intelligence Applications (DIA) Joint Laboratory（中国-上海合作组织数字智能应用联合实验室）；College of Engineering, Abu Dhabi University（阿布扎比大学工程学院）；Faculty of Engineering and Architecture, Nisantasi University（尼桑塔西大学工程与建筑学院）；School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）
Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子与信息学院）；College of Civil Aviation, Nanjing University of Aeronautics and Astronautics（南京航空航天大学民航学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；York University（约克大学）；University of Electronic Science and Technology of China（电子科技大学）；Sungkyunkwan University（成均馆大学）
Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, College of Computer Science, Northwest University（高级网络与智能信息服务省-市联合工程与研究中心，计算机科学学院，西北大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Institute of Artificial Intelligence, University of Science and Technology Beijing（人工智能研究院，北京科技大学）；Department of Electrical and Electronic Engineering, the University of Hong Kong（电子与电气工程系，香港大学）；Automation of School, Guangdong University of Technology（自动化学院，广东工业大学）；Queen’s University Belfast（贝尔法斯特女王大学）
