Accurate remote state estimation is a fundamental component of many autonomous and networked dynamical systems, where multiple decision-making agents interact and communicate over shared, bandwidth-constrained channels. These communication constraints introduce an additional layer of complexity, namely, the decision of when to communicate. This results in a fundamental trade-off between estimation accuracy and communication resource usage. Traditional extensions of classical estimation algorithms (e.g., the Kalman filter) treat the absence of communication as 'missing' information. However, silence itself can carry implicit information about the system's state, which, if properly interpreted, can enhance the estimation quality even in the absence of explicit communication. Leveraging this implicit structure, however, poses significant analytical challenges, even in relatively simple systems. In this paper, we propose CALM (Communication-Aware Learning and Monitoring), a novel learning-based framework that jointly addresses the dual challenges of communication scheduling and estimator design. Our approach entails learning not only when to communicate but also how to infer useful information from periods of communication silence. We perform comparative case studies on multiple benchmarks to demonstrate that CALM is able to decode the implicit coordination between the estimator and the scheduler to extract information from the instances of 'silence' and enhance the estimation accuracy.

翻译：精确的远程状态估计是许多自主与网络化动态系统的核心组成部分，其中多个决策智能体通过共享且带宽受限的通道进行交互与通信。这些通信约束引入了一层额外的复杂性，即何时进行通信的决策问题，从而在估计精度与通信资源使用之间形成了根本性的权衡。传统经典估计算法（如卡尔曼滤波器）的扩展通常将通信缺失视为‘信息丢失’。然而，沉默本身可能携带关于系统状态的隐含信息，若正确解读，即便在没有显式通信的情况下也能提升估计质量。然而，即使对于相对简单的系统，利用这种隐含结构也带来了显著的分析挑战。本文提出CALM（通信感知学习与监控），一种新颖的基于学习的框架，可协同应对通信调度与估计器设计的双重挑战。我们的方法不仅学习何时通信，还学习如何从通信静默期中推断有用信息。我们在多个基准测试上进行了对比案例研究，结果表明CALM能够解码估计器与调度器之间的隐含协调机制，从‘沉默’实例中提取信息，从而提升估计精度。