Remote state estimation, where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants' parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.
翻译:感应器将分布式动态工厂的测量结果传送到共享无线资源的遥控估计器,这种远距离状态估计是工业4.0 工业对任务至关重要的应用的关键。 现有的动态无线电资源配置算法对远程估计系统的动态无线电资源分配法假定了过度简化的无线通信模式,只能用于小规模的通信模式。 在这项工作中,我们考虑对正交多存和非正交存多存多存多存计划采用实用无线模型的远程估计系统; 我们从远程评估系统稳定的必要和充分条件中获取必要和充分的条件。 这些条件分别以传输电源预算、频道统计和工厂参数来描述。 对于每个多重接入计划,我们提出一个新的动态资源配置问题,作为实现最低总平均估计平均平均平均平均误差的决策问题。 我们考虑将估算质量和频道质量都纳入决策。 我们系统地调查了不同多存取计划下的问题,以大型离散、混合离散、连续和连续操作空间和连续操作空间系统为基础。 我们提出新的行动空间压缩方法,并发展先进的深度强化学习算法,以更好地解决资源配置问题。