The Silence that Speaks: Neural Estimation via Communication Gaps
Shubham Aggarwal, Dipankar Maity, Tamer Başar
TL;DR
The paper tackles remote state estimation over bandwidth-limited channels by jointly designing a scheduler and an estimator, and it reveals that periods of silence can carry meaningful information about the underlying stochastic dynamics. It introduces CALM, a deep reinforcement learning framework that alternates between training a scheduling policy via PPO and learning a nonlinear estimator that leverages silence, augmented by an age-of-information feature. The key contributions are the general treatment of nonlinear stochastic systems without restricting noise distributions, the demonstration that silence acts as a signal to improve estimation, and extensive benchmarks showing CALM outperforms traditional linear estimators and heuristic schedulers while maintaining lower communication costs. This work has practical implications for communication-constrained networked control in robotics, aerospace, and autonomous systems by enabling more efficient use of communication resources without sacrificing estimation fidelity.
Abstract
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.