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
5G/5G-Advanced在持续提升关键性能指标方面被寄予厚望,需要在时延、可靠性、连接数密度与用户体验等方面实现进一步突破。传统以人工操作为主的管理模式在效率、准确性与成本等方面的局限日益凸显。相较于传统优化方法,人工智能技术凭...5G/5G-Advanced在持续提升关键性能指标方面被寄予厚望,需要在时延、可靠性、连接数密度与用户体验等方面实现进一步突破。传统以人工操作为主的管理模式在效率、准确性与成本等方面的局限日益凸显。相较于传统优化方法,人工智能技术凭借其预测性与前瞻性,推动网络管理由被动应对转向主动感知与自优化,实现从“监测-响应”到“预判-编排”的迁移。基于3GPP在无线电接入网(radio access network,RAN)智能化方向的关键技术与标准化路径,结合典型用例场景,分析了AI/ML模型管理、数据采集与交互机制。面向6G智能RAN,进一步提出“意图驱动的协作任务”这一新型架构理念,其关键是通过RAN对应用层信息的感知、任务级别的服务质量(quality of service,QoS)监控、动态组和资源管理等技术实现6G网络人机及碳硅生态系统的无缝交互。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.
文摘5G/5G-Advanced在持续提升关键性能指标方面被寄予厚望,需要在时延、可靠性、连接数密度与用户体验等方面实现进一步突破。传统以人工操作为主的管理模式在效率、准确性与成本等方面的局限日益凸显。相较于传统优化方法,人工智能技术凭借其预测性与前瞻性,推动网络管理由被动应对转向主动感知与自优化,实现从“监测-响应”到“预判-编排”的迁移。基于3GPP在无线电接入网(radio access network,RAN)智能化方向的关键技术与标准化路径,结合典型用例场景,分析了AI/ML模型管理、数据采集与交互机制。面向6G智能RAN,进一步提出“意图驱动的协作任务”这一新型架构理念,其关键是通过RAN对应用层信息的感知、任务级别的服务质量(quality of service,QoS)监控、动态组和资源管理等技术实现6G网络人机及碳硅生态系统的无缝交互。
