With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the ...With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.展开更多
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 serv...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.展开更多
基金supported by the National Key Research and Development Program of China(2020YFB1807700)Innovation Capability Support Program of Shaanxi(2024RS-CXTD-01).
文摘With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2025JC-YBQN-820the Postdoctoral Science Foun-dation of Shaanxi Province under Grant 2024BSHSDZZ110+1 种基金the Fundamental Research Funds for the Central Universities under Grant ZYTS25265supported by the National Key Labora-tory of Multi-domain Data Collaborative Processing and Control(Pro-gram No.MDPC20240401)。
文摘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.
