Convergence of Fundamental Limitations in Feedback Communication, Estimation, and Feedback Control over Gaussian Channels
Jialing Liu, Nicola Elia

TL;DR
This paper unifies the fundamental limits of feedback communication, estimation, and control over Gaussian channels, revealing their deep interconnections through information, estimation, and control theory.
Contribution
It demonstrates that optimal feedback systems employ Kalman filters and links their performance metrics across communication, estimation, and control domains.
Findings
Optimal feedback communication uses Kalman filtering.
Information rate relates to CRB decay and Bode integral.
Tradeoffs involve causality, estimation errors, and disturbance rejection.
Abstract
In this paper, we establish the connections of the fundamental limitations in feedback communication, estimation, and feedback control over Gaussian channels, from a unifying perspective for information, estimation, and control. The optimal feedback communication system over a Gaussian necessarily employs the Kalman filter (KF) algorithm, and hence can be transformed into an estimation system and a feedback control system over the same channel. This follows that the information rate of the communication system is alternatively given by the decay rate of the Cramer-Rao bound (CRB) of the estimation system and by the Bode integral (BI) of the control system. Furthermore, the optimal tradeoff between the channel input power and information rate in feedback communication is alternatively characterized by the optimal tradeoff between the (causal) one-step prediction mean-square error (MSE)…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Target Tracking and Data Fusion in Sensor Networks · Gene Regulatory Network Analysis
