On a Hierarchical Content Caching and Asynchronous Updating Scheme for Non-Terrestrial Network-Assisted Connected Automated Vehicles

With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative v...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2025-01, Vol.43 (1), p.64-74
Main Authors: Mao, Bomin, Liu, Yangbo, Guo, Hongzhi, Xun, Yijie, Wang, Jiadai, Liu, Jiajia, Kato, Nei
Format: Article
Language:English
Subjects:
Ant colony optimization
asynchronous updating
Automation
Caching
Customization
Delays
hierarchical caching
Low earth orbit satellites
Low earth orbits
Mathematical models
multi-agent deep reinforcement learning
Multiagent systems
Non-terrestrial networks
Optimization
Quality of service
Quality of service architectures
Radio frequency interference
Satellite broadcasting
Satellites
Traffic capacity
Unmanned aerial vehicles
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Summary:With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2024.3460063