The Silence that Speaks: Neural Estimation via Communication Gaps

TL;DR

This paper introduces CALM, a learning-based framework that improves remote state estimation by intelligently interpreting communication silence as implicit information, balancing estimation accuracy and communication resource constraints.

Contribution

The paper presents a novel framework that jointly learns communication scheduling and inference from silence, addressing a key challenge in resource-constrained distributed estimation.

Findings

CALM effectively decodes implicit information from silence to improve estimation accuracy

The approach outperforms traditional methods in benchmark tests

Communication scheduling and inference are jointly optimized for better resource utilization

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.