Abstract:
Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-gen...Show MoreMetadata
Abstract:
Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. They offer significant benefits, including extending high-speed broadband coverage to remote and hard-to-reach areas. However, due to constraints like limited power and storage resources, SAGINs must be intelligently configured and managed to meet their envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to the recent advancements in AI techniques and hardware capabilities, AI has been leveraged to address the pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in developing AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.
Published in: IEEE Open Journal of the Communications Society ( Volume: 5)
Funding Agency:
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- IEEE Keywords
- Index Terms
- Network Integration ,
- Limited Power ,
- Wireless Networks ,
- Artificial Intelligence Applications ,
- Artificial Intelligence Techniques ,
- Storage Resources ,
- Potential Research Directions ,
- Resource Allocation ,
- Internet Of Things ,
- Unmanned Aerial Vehicles ,
- Channel Model ,
- Deep Reinforcement Learning ,
- Channel Estimation ,
- Artificial Intelligence Algorithms ,
- Reinforcement Learning Algorithm ,
- Satellite Communication ,
- Low Earth Orbit ,
- Deep Reinforcement Learning Algorithm ,
- Deep Q-network ,
- Terrestrial Networks ,
- Satellite Networks ,
- Unmanned Aerial Vehicles Deployment ,
- Unmanned Aerial Vehicles Networks ,
- Unmanned Aerial Vehicles Communication ,
- Computation Offloading ,
- Radio Resource Management ,
- Deep Reinforcement Learning Techniques ,
- Caching ,
- Long Short-term Memory ,
- Dynamic Environment
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Network Integration ,
- Limited Power ,
- Wireless Networks ,
- Artificial Intelligence Applications ,
- Artificial Intelligence Techniques ,
- Storage Resources ,
- Potential Research Directions ,
- Resource Allocation ,
- Internet Of Things ,
- Unmanned Aerial Vehicles ,
- Channel Model ,
- Deep Reinforcement Learning ,
- Channel Estimation ,
- Artificial Intelligence Algorithms ,
- Reinforcement Learning Algorithm ,
- Satellite Communication ,
- Low Earth Orbit ,
- Deep Reinforcement Learning Algorithm ,
- Deep Q-network ,
- Terrestrial Networks ,
- Satellite Networks ,
- Unmanned Aerial Vehicles Deployment ,
- Unmanned Aerial Vehicles Networks ,
- Unmanned Aerial Vehicles Communication ,
- Computation Offloading ,
- Radio Resource Management ,
- Deep Reinforcement Learning Techniques ,
- Caching ,
- Long Short-term Memory ,
- Dynamic Environment
- Author Keywords