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基于智能分层切片技术的数字孪生传感信息同步策略

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针对传感数据在无线接入网(RAN)中传输的不可靠性与不及时性造成数字孪生(DTs)同步信息的不精确问题,该文提出一种基于智能分层切片技术的DTs传感信息同步策略.该策略在双时间尺度下,以最大化传感信息满意度和最小化切片重配置及DTs同步成本为目标,联合优化切片无线资源配置以及DTs传感信息同步问题.首先,在大时间尺度,利用网络切片为有着不同服务质量(QoS)的DTs提供隔离以及解决部署问题;在小时间尺度,通过更加灵活的无线资源分配来提高DTs传感信息同步任务对动态环境的适应性,进一步提高通信性能,建立更逼近于物理实体的DTs.其次,为了求解不同时间尺度的优化问题,该文提出一种双层深度强化学习(DRL)框架实现高效的网络资源交互,其中下层控制算法利用优先经验放回(PER)机制加快收敛速度.最后,仿真结果验证了所提策略的有效性.
Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique
In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins(DTs)caused by unreliable and delayed transmission in Radio Access Networks(RAN),a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed.The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs'sensing information in dual time scales,with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs'synchronization.Firstly,at large time scales,network slicing is employed to provide isolation for DTs with varying Quality of Service(QoS)and resolve deployment challenges;At small time scales,a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs'sensory information synchronization to dynamic environments.Secondly,in order to optimize the synchronization of DTs'sensory information at different time scales,a two-layer Deep Reinforcement Learning(DRL)framework is introduced to facilitate efficient network resource interaction,and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay(PER)mechanism to accelerate convergence speed.Finally,the effectiveness of the proposed strategy is validated through simulation results.

Digital Twin(DT)Network sliceDeep Reinforcement Learning(DRL)State estimationResource allocation

唐伦、李质萱、文雯、成章超、陈前斌

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重庆邮电大学通信与信息工程学院 重庆 400065

移动通信技术重庆市重点实验室 重庆 400065

数字孪生 网络切片 深度强化学习 状态估计 资源分配

国家自然科学基金四川省科技计划

620710782021YFQ0053

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(7)