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Space-Air-Ground-Sea Integrated Communication Networks, Second Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 350

Special Issue Editors

College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: maritime communications; space–air–ground–sea integrated communication; edge computing; resource management
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: marine internet; best-effort networking systems; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the expansion of human activity and the rise in intelligent applications, communication network services need lower latency, higher capacity, and seamless coverage anywhere and anytime. Space–Air–Ground–Sea Integrated Network (SAGSIN) integrates space-based networks, air-based networks, and ground-based, as well as sea-based networks to form a much more complex system that can significantly enhance communication coverage and improve network service ability.

SAGSIN has attracted great attention on its architecture, resource management, communication network protocols, simulation studies, and device demo design. To promote research on SAGSIN and the development of related industries, we organize the topic of ‘Space–Air–Ground–Sea Integrated Communication Networks’ in Entropy to collect and publish recent innovative research and engineering application results. This Special Issue is collaborating with the International Conference on Marine Communications, Networks and Signal Processing (MCNSP 2026), which will be held on March 27–29, 2026, in Sanya, China.

We welcome relevant experts, scholars, and researchers to submit their contributions. You are invited to submit papers and review articles are related, but not limited, to the following topics of interests:

  1. network architecture and protocols of SAGSIN;
  2. performance analysis based on information theory for SAGSIN;
  3. self-organization schemes of SAGSIN;
  4. resource allocation and management of SAGSIN;
  5. cognitive radio of SAGSIN;
  6. cloud computation and edge computation of SAGSIN;
  7. interconnection and cooperation between heterogeneous networks in SAGSIN;
  8. QoS-aware algorithms, interference mitigation algorithms, and energy efficiency algorithms for SAGSIN;
  9. cross-domain topology discovery of heterogeneous networks;
  10. machine learning technologies for SAGSIN;
  11. new information theory for SAGSIN such as semantic communication;
  12. network optimization based on game theory and other optimization tools;
  13. simulation and experiment studies of SAGSIN.

Dr. Yanli Xu
Prof. Dr. Shengming Jiang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • space–air–ground–sea integrated networks
  • resource management
  • topology discovery
  • network access protocols
  • communication protocols
  • network deployment
  • UAV ad hoc networks
  • satellite communications
  • maritime communications

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Published Papers (1 paper)

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Research

30 pages, 1591 KB  
Article
Joint Optimization of User Association and Dynamic Multi-UAV Deployment for Maritime Emergency Communications
by Xiaonan Ma, Hua Yang, Yanli Xu and Naoki Wakamiya
Entropy 2026, 28(5), 561; https://doi.org/10.3390/e28050561 - 17 May 2026
Viewed by 97
Abstract
Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide [...] Read more.
Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article
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