Enabling intent-driven CoX mechanism in space-terrestrial network for multiple mission impossible
Year: 2025
Type: article
Abstract: The Space-Terrestrial Network (STN) aims to deliver comprehensive on-demand network services, addressing the broad and varied needs of Internet of Things (IoT) applications. However, the STN faces new challenges such as service multiplicity, topology dynamicity, and conventional management complexity. This necessitates a flexible and autonomous approach to network resource management to effectively align network services with available resources. Thus, we incorporate the Intent-Driven Network (IDN) into the STN, enabling the execution of multiple missions through automated resource allocation and dynamic network policy optimization. This approach enhances programmability and flexibility, facilitating intelligent network management for real-time control and adaptable service deployment in both traditional and IoT-focused scenarios. Building on previous mechanisms, we develop the intent-driven CoX resource management model, which includes components for coordination intent decomposition, collaboration intent management, and cooperation resource management. We propose an advanced intent verification mechanism and create an intent-driven CoX resource management algorithm leveraging a two-stage deep reinforcement learning method to minimize resource usage and delay costs in cross-domain communications within the STN. Ultimately, we establish an intent-driven CoX prototype to validate the efficacy of this proposed mechanism, which demonstrates improved performance in intent refinement and resource management efficiency.
Authors: Ying Ouyang, Chungang Yang, Rongqian Fan, Tangyi Li
Institution: Xidian University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $800
Awards: G7295170839, G7417728132, G7923795942
OpenAlex ID: W4411730817