mlsanityData to 7 Oct 2026

Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions

arXiv 2407.04581 ↗First posted 5 Jul 2024cs.LG cs.ETcited by 10

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Abstract (arXiv)

Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies to ensure seamless connectivity across different altitudes and platforms. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance these networks. We outline the current architecture of ISATNs and highlight the significant role LLMs can play in optimizing data flow, signal processing, and network management to advance 5G/6G communication technologies through advanced predictive algorithms and real-time decision-making. A comprehensive analysis of ISATN components is conducted, assessing how LLMs can effectively address traditional data transmission and processing bottlenecks. The paper delves into the network management challenges within ISATNs, emphasizing the necessity for sophisticated resource allocation strategies, traffic routing, and security management to ensure seamless connectivity and optimal performance under varying conditions. Furthermore, we examine the technical challenges and limitations associated with integrating LLMs into ISATNs, such as data integration for LLM processing, scalability issues, latency in decision-making processes, and the design of robust, fault-tolerant systems. The study also identifies key future research directions for fully harnessing LLM capabilities in ISATNs, which is crucial for enhancing network reliability, optimizing performance, and achieving a truly interconnected and intelligent global network system.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 94 of its 237 references that are in the map. First posted on arXiv 5 Jul 2024.

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94 of 237 references.

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38 more
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50 most cited of 94 All 94 (JSON)

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Cited by year

Citing papers per year: 2024: 1; 2025: 9.

10 papers.

  1. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  2. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  3. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  4. 2025Empowering Intelligent Low-altitude Economy with Large AI Model Deploymentcited by 3
  5. 2025Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillationcited by 3
  6. 2025An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learningcited by 2
  7. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  8. 2025BERT4beam: Large AI Model Enabled Generalized Beamforming Optimizationcited by 1
  9. 2025Carbon-Aware Orchestration of Integrated Satellite Aerial Terrestrial Networks via Digital Twincited by 0
  10. 2025Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challengescited by 0