标题： 基于扩散模型的超高可靠性无线网络化控制系统资源分配策略 显示英文标题

标题： Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems

摘要： 扩散模型在生成式人工智能中得到了广泛应用，利用了其捕捉复杂数据分布的能力。然而，在无线网络资源分配领域，其潜力仍未得到充分探索。本文提出了一种基于新型扩散模型的资源分配策略，用于无线网络控制系统（WNCS），旨在通过优化控制系统的采样周期以及通信系统有限块长下的块长和数据包误码概率来最小化总能耗。首先，问题被简化为仅优化块长，基于最优性条件的推导完成。接着，优化理论解法收集了一组信道增益及其对应的最优块长的数据集。最后，去噪扩散概率模型（DDPM）使用该收集的数据集训练资源分配算法，生成以信道状态信息（CSI）为条件的最优块长值。通过广泛的仿真表明，所提出的方案在总能耗方面表现出接近最优性能，并且比先前提出的深度强化学习（DRL）方法表现更优。此外，还观察到关键约束违规减少幅度提高了多达十八倍，进一步突显了解决方案的准确性。 显示英文摘要

摘要： Diffusion models are vastly used in generative AI, leveraging their capability to capture complex data distributions. However, their potential remains largely unexplored in the field of resource allocation in wireless networks. This paper introduces a novel diffusion model-based resource allocation strategy for Wireless Networked Control Systems (WNCSs) with the objective of minimizing total power consumption through the optimization of the sampling period in the control system, and blocklength and packet error probability in the finite blocklength regime of the communication system. The problem is first reduced to the optimization of blocklength only based on the derivation of the optimality conditions. Then, the optimization theory solution collects a dataset of channel gains and corresponding optimal blocklengths. Finally, the Denoising Diffusion Probabilistic Model (DDPM) uses this collected dataset to train the resource allocation algorithm that generates optimal blocklength values conditioned on the channel state information (CSI). Via extensive simulations, the proposed approach is shown to outperform previously proposed Deep Reinforcement Learning (DRL) based approaches with close to optimal performance regarding total power consumption. Moreover, an improvement of up to eighteen-fold in the reduction of critical constraint violations is observed, further underscoring the accuracy of the solution.