Source-linked AI summary
Enabling 5G on the Ocean: A Hybrid Satellite-UAV-Terrestrial Network Solution
Xiangling Li, Wei Feng, Jue Wang, Yunfei Chen, Ning Ge, Cheng-Xiang Wang
TL;DR
Maritime networks provide much lower data rates than terrestrial 5G because ocean infrastructure is difficult to deploy. The paper studies integrating agile UAVs with satellites and shore-based TBSs for on-demand coverage, showing benefits through a constrained numerical case study while identifying coordination and channel-state challenges.
Problem
Maritime communication networks provide only a few Mbps, far below 5G’s Gbps scale, while deploying infrastructure across the ocean is difficult.
Method
The paper integrates UAVs with existing satellites and shore-based TBSs, using vessel mobility, UAV scheduling, predictable large-scale CSI, and joint optimization under practical constraints.
Results
The case study shows that UAV assistance can improve minimum ergodic achievable rate despite backhaul-capacity and inaccurate-CSI restrictions, with performance shaped by energy, backhaul, transmit-power, and interference constraints.
Takeaways & Limitations
Sparse, sea-lane-following vessels and UAV agility support on-demand maritime coverage through dynamic deployment and scheduling coordinated with TBSs and satellites.
Abstract
from arXiv · showhide
Current fifth generation (5G) cellular networks mainly focus on the terrestrial scenario. Due to the difficulty of deploying communications infrastructure on the ocean, the performance of existing maritime communication networks (MCNs) is far behind 5G. This problem can be solved by using unmanned aerial vehicles (UAVs) as agile aerial platforms to enable on-demand maritime coverage, as a supplement to marine satellites and shore-based terrestrial based stations (TBSs). In this paper, we study the integration of UAVs with existing MCNs, and investigate the potential gains of hybrid satellite-UAV-terrestrial networks for maritime coverage. Unlike the terrestrial scenario, vessels on the ocean keep to sea lanes and are sparsely distributed. This provides new opportunities to ease the scheduling of UAVs. Also, new challenges arise due to the more complicated maritime prorogation environment, as well as the mutual interference between UAVs and existing satellites/TBSs. We discuss these issues and show possible solutions considering practical constraints.
I. INTRODUCTION
Maritime communications lag far behind terrestrial 5G because ocean infrastructure is difficult to deploy. The paper proposes integrating agile UAVs with satellites and shore-based TBSs to provide on-demand coverage while addressing maritime propagation and interference challenges.
- Maritime data rates have approached a few Mbps, far below the Gbps scale supported by 5G cellular networks.
- Shore-based TBSs extend coverage tens of kilometers offshore, while satellite links reach remote areas but generally provide lower data rates.
- UAVs offer more agile, lower-altitude mobility than balloons for dynamic maritime coverage enhancement.
- The proposed hybrid network dispatches UAVs to fill coverage holes beyond conventional TBS and satellite coverage.
- The integration must address complicated maritime propagation and mutual interference among UAVs, TBSs, and satellites.
A. Agile Mobility of UAVs
UAVs provide the most flexible deployment option among TBSs, satellites, and aerial platforms, but limited endurance and weather constraints require careful scheduling.
- Mobility comparison: TBS deployment is fixed along coastlines, while shipborne base stations remain constrained by sea-lane navigation safety.
- Mobility comparison: Satellite deployment follows constrained orbits, making expensive LEO constellations generally necessary for global coverage.
- Mobility comparison: UAVs have the most flexible deployment and can fly with target vessels to provide on-demand broadband services.
- Mobility comparison: Limited onboard energy and weather conditions restrict UAV endurance and deployment, motivating scheduling under practical constraints.
- Flying closer to users can improve transmission rate and shorten communication latency, while predictable maritime mobility can improve UAV efficiency.
B. Unique Characteristics of Maritime Users
Maritime vessels are spatially and temporally sparse, with most following regular sea lanes rather than moving randomly. These predictable patterns enable efficient tracking and UAV scheduling.
- Vessel distribution: AIS data characterizes vessels distributed across coastal areas 20–30 km offshore and across the specified observation period.
- Vessel mobility: Most vessels follow fixed shipping lanes, although some vessels move randomly.
- Vessel data: AIS is a ship transponder system that regularly reports position, heading, speed, and other state information.
- Vessel mobility: Sea-lane curves were accumulated from AIS data for 610 vessels observed during one hour.
- Scheduling opportunity: Regular and predictable mobility patterns allow vessels to be tracked across the ocean and create opportunities for efficient UAV scheduling.
C. On-Demand Coverage by Maritime UAVs
The paper uses maritime vessel mobility and UAV agility to formulate on-demand coverage: UAVs serve vessels outside existing coverage, then return or continue to the next queued user.
- A UAV can be dispatched to a vessel requiring broadband service outside existing MCN coverage.
- The UAV may either serve and leave or move with the vessel to provide long-term broadband service.
- After completing transmission, the UAV returns to a charging station or travels toward the next vessel in the service queue.
- Dynamic deployment over sea lanes and scheduling for intermittent demand can improve the efficiency of maritime communications.
III. CHALLENGES AND POSSIBLE SOLUTIONS
Integrating UAVs into maritime communication networks faces three main challenges: harsh environmental conditions, coordination requirements, and limited channel state information.
- Harsh maritime weather can affect the real-time deployment of UAVs.
- Hybrid network integration requires joint resource allocation and interference coordination.
- Dynamic propagation and large transmission delays limit channel state information and complicate system optimization.
A. Harsh Maritime Environment
Maritime UAV deployment is constrained by severe weather, limited opportunities for landing and charging, and finite onboard energy. The paper proposes combining offline prediction with online adaptation and coordinated UAV replacement to maintain coverage.
- Typhoon winds can exceed 30 m/s, while most existing UAVs are designed for wind speeds below 17.1 m/s.UAV selection must therefore account for maritime weather conditions.
- Historical communication-demand data over sea lanes can identify hotspot areas for pre-deploying UAVs within their endurance time.Pre-deployment can reduce serving latency relative to request-triggered dispatch.
- UAV deployment is restricted by the difficulty of landing and charging across vast sea areas.The paper identifies offline deployment and service-station planning as responses to this constraint.
- UAVs typically fly for less than 8 hours, so residual energy and energy-replenishment locations must guide deployment.Vessels can serve as replenishment stations, but their locations are restricted to sea lanes.
- Idle neighboring UAVs with sufficient residual energy can replace UAVs traveling to service stations, supporting continuous coverage.
B. Coordination Issues
Maritime UAVs must coordinate backhaul and spectrum use with existing terrestrial and satellite networks. Limited offshore backhaul and mobility-induced interference make scheduling and coordination more complex.
- Offshore UAVs may depend on satellites for backhaul because shore-based TBSs provide support mainly near the coast.Satellite backhaul introduces large delay and limited communication rate, and requires airborne high-gain antennas.
- Backhaul constraints should be incorporated into UAV scheduling, while onboard data caching can tolerate interim backhaul outages when information permits.
- UAV spectrum sharing with existing MCNs creates more complicated co-channel interference because UAVs are mobile.Trajectory-based prediction can support process-oriented interference coordination.
C. Limited Channel State Information
Maritime UAV planning requires channel state information, but offline trajectories, absent direct feedback links, and exchange delays make instantaneous CSI difficult to obtain. The paper therefore considers predictable large-scale CSI and radio maps for optimization.
- Maritime UAV trajectory optimization may need predictable CSI because trajectories are often predetermined offline.
- CSI between UAVs and satellite users may require exchange through a central processor, causing undesirable delay because direct feedback links are usually absent.
- Large-scale CSI, including path loss, shadowing, and antenna angles, varies slowly and is related to transceiver positions.Historical or premeasured data can be used to predict these quantities.
- A radio map focused on shipping lanes can generate large-scale CSI for given positions and support hybrid-network optimization.Dedicated UAVs and vessels can initially measure the large-scale CSI.
IV. NUMERICAL EXAMPLE AND DISCUSSIONS
The numerical example evaluates on-demand UAV assistance for maritime coverage using a composite fading model and joint trajectory–power optimization. It shows that a tailored maritime scheduling algorithm improves the minimum ergodic achievable rate despite backhaul and CSI limitations, with performance governed by different practical constraints across settings.
- On-demand deployment: UAVs are dispatched on demand to serve vessels, following trajectories pre-designed from shipping-lane and predicted large-scale channel information.After transmission, the UAV returns to its service station.
- Channel and information model: The channel model combines path loss and Rician fading, with only large-scale CSI available for UAV pre-deployment.
- Optimization problem: Trajectory and transmit power are jointly optimized to maximize the minimum ergodic achievable rate under power, energy, backhaul, and interference-temperature constraints.This objective targets worst-case coverage performance.
- Coverage comparison: UAV assistance can improve the minimum ergodic achievable rate over shore-based MCN service by reducing transmission distance, despite inaccurate CSI and limited backhaul.The shore-based comparison assumes direct TBS service with accurate CSI.
- Scheduling comparison: The maritime scheduling algorithm using only large-scale CSI achieves better performance than the terrestrial-oriented alternative under maritime constraints.The comparison includes interference and maximum-transmit-power considerations.
- Constraint effects: When Pmax ≥28 dBm, I = −40 dBm, and E = 1.5 × 10^3 J, increasing Pmax no longer changes performance, which is mainly determined by residual energy and backhaul capacity.With E = 3 × 10^4 J, residual-energy constraints can be ignored; increasing I improves performance when interference constraints are active.
V. CONCLUSIONS
The paper identifies UAV integration as a way to exploit sparse, lane-following maritime traffic for on-demand coverage. It frames coordination and large-scale-CSI optimization as responses to maritime constraints and demonstrates benefits through a hybrid-network case study.
- Opportunities: Most vessels are sparsely distributed and keep to sea lanes, creating opportunities for on-demand UAV coverage.
- Challenges and solutions: Dynamic UAV deployment and scheduling address maritime challenges through coordination among TBSs, UAVs, and satellites and optimization using predictable large-scale CSI.
- Case study: A case study demonstrates benefits provided by the hybrid satellite-UAV-terrestrial network.