发表机构
Università di Pisa; King Abdullah University of Science and Technology (KAUST)（比萨大学; 阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SMCC-DT框架，利用6G边缘ISAC波形实现无传感器监测，通过跨层优化和PPO强化学习智能体，在500节点测试台上将数字孪生同步延迟降低38.7%，能耗降低27.4%。
AI 中文摘要
在大型物联网（IoT）生态系统中部署AI驱动的数字孪生（DT），要求物理环境与其虚拟副本之间进行持续、高保真的同步。传统方法依赖密集的传感器部署，这在硬件、能源和网络带宽方面引入了高昂的成本。在本文中，我们提出了SMCC-DT，一个集成的感知-记忆-通信-计算（SMCC）框架，通过利用6G边缘的集成感知与通信（ISAC）波形，实现对物理资产的无传感器监测。在SMCC-DT范式下，单一无线电信号同时提取环境遥测数据（感知）并将其传输到边缘服务器（通信），在边缘服务器上，大规模AI模型被加载到受限内存（记忆）中并执行（计算）以更新数字孪生状态。我们将数字孪生同步问题表述为跨层优化，该优化联合分配发射功率、波束成形向量、内存分区和CPU频率，以在感知精度、吞吐量、内存容量和计算预算约束下最小化端到端同步延迟。由于由此产生的混合整数非线性规划问题是NP难的，我们设计了一个基于近端策略优化（PPO）的深度强化学习（DRL）智能体，称为SMCCAGENT，它在线学习接近最优的资源分配策略。在500节点工业物联网测试台上进行的大量模拟表明，与最先进的正交和纯计算基线相比，SMCC-DT将数字孪生同步延迟降低了38.7%，总能耗降低了27.4%，同时保持感知精度高于95%，模型推理吞吐量高于每秒30帧。
英文摘要
The deployment of AI-driven Digital Twins (DTs) in large-scale Internet-of-Things (IoT) ecosystems demands continuous, high-fidelity synchronization between the physical environment and its virtual replica. Conventional approaches rely on dense sensor deployments, which introduce prohibitive costs in terms of hardware, energy, and network bandwidth. In this paper, we propose SMCC-DT, an integrated Sensing-Memory-Communication-Computation (SMCC) framework that enables sensorless monitoring of physical assets by exploiting Integrated Sensing and Communication (ISAC) waveforms at the 6G Edge. Under the SMCC-DT paradigm, a single radio signal simultaneously extracts environmental telemetry (Sensing) and delivers it to an Edge server (Communication), where a large-scale AI model is loaded into constrained memory (Memory) and executed (Computation) to update the DT state. We formulate the DT synchronization problem as a cross-layer optimization that jointly allocates transmit power, beamforming vectors, memory partitions, and CPU frequency to minimize the end-to-end synchronization latency subject to sensing accuracy, throughput, memory capacity, and computational budget constraints. Because the resulting mixed-integer nonlinear program is NP-hard, we design a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) agent, termed SMCCAGENT, that learns near-optimal resource allocation policies online. Extensive simulations over a 500-node industrial IoT testbed demonstrate that SMCC-DT reduces DT synchronization latency by 38.7% and total energy consumption by 27.4% compared to state-of-the-art orthogonal and compute-only baselines, while sustaining sensing accuracy above 95% and model inference throughput above 30 frames per second.
CommentsThis work has been submitted to the IEEE INTERNET OF THINGS JOURNAL for possible publication
