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Interplay Between AI and Space-Air-Ground Integrated Network: The Road Ahead

Chenyu Wu, Xi Wang, Yi Hu, Shuai Han, Dusit Niyato

TL;DR

This work addresses the challenge of coordinating AI with Space-Air-Ground Integrated Networks (SAGIN) to enable ubiquitous 6G connectivity. It argues that task-specific AI approaches are insufficient for SAGIN's dynamic, multi-layer topology and proposes a generalized big AI model (BAIM) and generative AI (GenAI) integrated with SDN/NFV to manage SAGIN end-to-end. A central contribution is the AI-SFCO framework for multi-domain SFC orchestration, featuring intra-domain controllers and an AI-driven inter-domain coordinator, supported by a disaster-relief case study that demonstrates improved service completion and revenue. The paper highlights the potential for scalable, cross-domain automation in future wireless networks and outlines a roadmap for deploying AI-driven SAGIN management in real-world deployments.

Abstract

Space-air-ground integrated network (SAGIN) is envisioned as a key network architecture for achieving ubiquitous coverage in the next-generation communication system. Concurrently, artificial intelligence (AI) plays a pivotal role in managing the complex control of SAGIN, thereby enhancing its automation and flexibility. Despite this, there remains a significant research gap concerning the interaction between AI and SAGIN. In this context, we first present a promising approach for developing a generalized AI model capable of executing multiple tasks simultaneously in SAGIN. Subsequently, we propose a framework that leverages software-defined networking (SDN) and AI technologies to manage the resources and services across the entire SAGIN. Particularly, we demonstrate the real-world applicability of our proposed framework through a comprehensive case study. These works pave the way for the deep integration of SAGIN and AI in future wireless networks.

Interplay Between AI and Space-Air-Ground Integrated Network: The Road Ahead

TL;DR

This work addresses the challenge of coordinating AI with Space-Air-Ground Integrated Networks (SAGIN) to enable ubiquitous 6G connectivity. It argues that task-specific AI approaches are insufficient for SAGIN's dynamic, multi-layer topology and proposes a generalized big AI model (BAIM) and generative AI (GenAI) integrated with SDN/NFV to manage SAGIN end-to-end. A central contribution is the AI-SFCO framework for multi-domain SFC orchestration, featuring intra-domain controllers and an AI-driven inter-domain coordinator, supported by a disaster-relief case study that demonstrates improved service completion and revenue. The paper highlights the potential for scalable, cross-domain automation in future wireless networks and outlines a roadmap for deploying AI-driven SAGIN management in real-world deployments.

Abstract

Space-air-ground integrated network (SAGIN) is envisioned as a key network architecture for achieving ubiquitous coverage in the next-generation communication system. Concurrently, artificial intelligence (AI) plays a pivotal role in managing the complex control of SAGIN, thereby enhancing its automation and flexibility. Despite this, there remains a significant research gap concerning the interaction between AI and SAGIN. In this context, we first present a promising approach for developing a generalized AI model capable of executing multiple tasks simultaneously in SAGIN. Subsequently, we propose a framework that leverages software-defined networking (SDN) and AI technologies to manage the resources and services across the entire SAGIN. Particularly, we demonstrate the real-world applicability of our proposed framework through a comprehensive case study. These works pave the way for the deep integration of SAGIN and AI in future wireless networks.
Paper Structure (16 sections, 3 figures, 2 tables)

This paper contains 16 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. AI Applications for SAGIN Optimization
  3. Representative Use Cases
  4. Resource Allocation
  5. Task Offloading
  6. Routing
  7. Environment-Aware and Adaptive Communications
  8. Design Guideline and Challenges
  9. AI Applications for SAGIN Management
  10. Framework of AI-SFCO
  11. Challenges and Solutions
  12. Service Performance Guarantee
  13. Dynamic Topology Adaption
  14. Cross-Domain Coordination
  15. A Case Study
  16. ...and 1 more sections

Figures (3)

  • Figure 1: A generalized framework based on big generative AI for tackling various tasks in SAGIN. Representative use cases include resource allocation, task offloading, routing, and environment-aware and adaptive communication.
  • Figure 2: AI-SFCO: the framework for managing multi-domain SAGIN. The intra-domain controller handles the SFC orchestration according to the network status, while the inter-domain controller is responsible for the communication and collaboration between different management domains.
  • Figure 3: Simulation results for the deployment of SFCs in the SAGIN. Key A3C-related parameters are summarized as follows: Number of actors: 4; learning rates of the actor and critic networks: 0.0025 and 0.0005, respectively; discount factor for temporal difference error: 0.95; batch size for experience replay: 64; number of hidden units per layer: 64.