AI-Driven Maritime–Aerial Heterogeneous Fusion Networks for Next-Generation Communications
A special issue of Network (ISSN 2673-8732).
Deadline for manuscript submissions: 31 December 2026 | Viewed by 216
Editors
Interests: underwater network; Internet of Things; network security; intelligent algorithms
Special Issue Information
Dear Colleagues,
This Special Issue aims to present recent advances in AI-driven maritime–aerial heterogeneous fusion networks that integrate diverse communication paradigms, including 5G/6G systems, satellite networks, and ad hoc platforms, into a unified and intelligent architecture. Maritime communication environments are characterized by highly dynamic network topologies, frequent link interruptions, high node mobility, and significant heterogeneity in bandwidth, latency, and coverage. These inherent challenges require adaptive, data-driven, and self-optimizing networking mechanisms beyond conventional static designs.
We seek contributions that explore intelligent multi-radio access technology (Multi-RAT) architectures and cross-domain networking solutions for maritime–aerial integration. Topics of interest include the design of AI-enabled Multi-RAT gateways for seamless protocol translation and semantic-aware information exchange; collaborative resource orchestration across heterogeneous communication systems; and hybrid centralized–distributed traffic control frameworks that enable scalable and resilient network operations. Particular emphasis is placed on learning-based solutions for dynamic topology reconstruction, mobility management under intermittent connectivity, and multipath transmission strategies for reliable and redundant data delivery. Contributions that investigate intelligent routing optimization to extend network lifetime and improve Quality of Service under fluctuating channel and traffic conditions are particularly encouraged.
In alignment with next-generation communication paradigms, this Special Issue particularly welcomes research that incorporates deep reinforcement learning, graph neural networks, federated learning, and other AI techniques to enable adaptive decision-making based on real-time network states. By bridging communication theory, network architecture, and artificial intelligence, this Special Issue seeks to advance the development of self-evolving maritime–aerial fusion networks capable of maintaining stable, efficient, and resilient performance under heavy traffic loads and extreme environmental conditions.
Dr. Rongxin Zhu
Dr. Haokun Yang
Guest Editors
Manuscript Submission Information
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Keywords
- maritime–aerial integrated networks
- heterogeneous fusion networks
- multi-radio access technologies (multi-RAT)
- AI-driven networking
- intelligent routing
- deep reinforcement learning
- graph neural networks
- cross-domain communication
- adaptive resource management
- resilient network architecture
- dynamic topology reconstruction
- multipath transmission
- quality of service assurance
- next-generation communications
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