Cheng.Pavone.ea.Neurips.2021.tex

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\input{sections/preamble}

\title{Data Sharing and Compression for Cooperative Networked Control}

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\author[1]{Jiangnan Cheng}
\author[2]{Marco Pavone}
\author[3]{Sachin Katti}
\author[4]{Sandeep Chinchali}
\author[1]{Ao Tang}

\affil[1]{School of Electrical and Computer Engineering, Cornell University, Ithaca, NY}
\affil[2]{Department of Aeronautics and Astronautics, Stanford University, Stanford, CA}
\affil[3]{Department of Computer Science, Stanford University, Stanford, CA}
\affil[4]{Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX \authorcr
  \{\tt jc3377, atang\}@cornell.edu, \{\tt pavone, skatti\}@stanford.edu, \tt sandeepc@utexas.edu}

% \author{%
%   Jiangnan Cheng\\
%   Cornell University\\
%   \And
%   Marco Pavone\\
%   Stanford University\\
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%   Sachin Katti\\
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%   Sandeep Chinchali\\
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%   Ao Tang\\
%   Cornell University\\
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\begin{document}

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\begin{abstract}
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for \textit{mean} prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are \textit{co-designed} with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least $25\%$ while transmitting $80\%$ less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.
\end{abstract}

\section{Introduction}
\label{sec:intro}
\input{sections/intro.tex}

\section{Problem Formulation}
\label{sec:problem_statement}
\input{sections/problem_formulation.tex}

\section{Forecaster and Controller Co-design}
\label{sec:algorithm}
\input{sections/algorithm.tex}

\section{Application Scenarios}
\label{sec:scenarios}
\input{sections/scenarios.tex}

\section{Evaluation}
\label{sec:evaluation}
\input{sections/experiments.tex}

\section{Conclusion}
\label{sec:conclusion}
\input{sections/conclusion.tex}

\section{Funding Disclosure}
\label{sec:funding}
\input{sections/funding_disclosure.tex}

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\newpage
\section*{Checklist}
\input{sections/checklist.tex}

\newpage
\appendix
\section{Appendix}
\input{sections/appendix.tex}


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