A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Abstract

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

Design and Experiment of Satellite-Terrestrial Integrated Gateway with Dynamic Traffic Steering Capabilities for Maritime Communication

“…The maritime autonomous surface ship (MASS) is also one of the main research streams for the digital transformation of the maritime sector [ 27 , 28 , 29 ] and there are ongoing works to enable advanced communication technologies including satellites, Unmanned Aerial Vehicles (UAVs), and IMT systems (e.g., 5G, 6G) with improved coverage and data rates to be applied for the MASS [ 30 , 31 , 32 , 33 , 34 ].…”

Joint optimization of UAV position and user grouping for UAV‐assisted hybrid NOMA systems

“…Although the receiver using SIC technology has a certain complexity, it can improve the spectral efficiency well. Thus, the UVA-assisted NOMA (UAV-NOMA) 3 system has the advantages of both UAV and NOMA. Compared with traditional communication systems, UAV can move in three-dimensional (3D) space, and NOMA can provide higher spectrum efficiency and more connections, which can further improve the overall system performance.…”

“…Recently, UAV-NOMA have been studied in the offshore iot topology, 3,4 where multiple UAVs are used in Reference 3 for end-to-end connectivity while receiving data from offshore internet of things (IoT) nodes. Taking into account, the rate requirements and channel state information (CSI) of each offshore node to the UAVs, dynamic decoding sequencing is used to improve the performance of SIC.…”

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

“…In addition, the huge amount of data generated by the devices of the IoT can be used to extract useful information through machine learning, thus achieving a variety of intelligent IoT services. Emerging applications of the intelligent IoT include automobile driving [7]- [8], unmanned aerial vehicles (UAV) [9]- [10], robots [11], health care [12], supply chain finance, and so on. However, in the 6G era, the paradigm shift from 'interconnected things' to 'interconnected intelligence' through modern machine learning technology is facing three major challenges.…”