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Optimal Gaussian Strategies for Vector-valued Witsenhausen Counterexample with Non-causal State Estimator
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0009-0005-3227-8917
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-0036-9049
University of Rennes, CNRS, Inria, IRISA UMR 6074, Rennes, France, F-35000.
2024 (English)In: 2024 IEEE 63rd Conference on Decision and Control, CDC 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 8447-8452Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we investigate a vector-valued Witsenhausen model where the second decision-maker (DM) acquires a vector of observations before selecting a vector of estimations. Here, the first DM acts causally whereas the second DM estimates non-causally. When the vector length grows, we characterize, via a single-letter expression, the optimal tradeoff between the power cost at the first DM and the estimation cost at the second DM. In this paper, we show that the best linear scheme is achieved by using the time-sharing method between two affine strategies, which coincides with the convex envelope of the solution of Witsenhausen in 1968. Here also, Witsenhausen's two-point strategy and the scheme of Grover and Sahai in 2010 where both devices operate non-causally, outperform our best linear scheme. Therefore, gains obtained with block-coding schemes are only attainable if all DMs operate non-causally.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. p. 8447-8452
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-361771DOI: 10.1109/CDC56724.2024.10886216Scopus ID: 2-s2.0-86000524594OAI: oai:DiVA.org:kth-361771DiVA, id: diva2:1948038
Conference
63rd IEEE Conference on Decision and Control, CDC 2024, Milan, Italy, Dec 16 2024 - Dec 19 2024
Note

Part of ISBN 9798350316339

QC 20250401

Available from: 2025-03-27 Created: 2025-03-27 Last updated: 2025-04-01Bibliographically approved

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Zhao, MengyuanOechtering, Tobias J.

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CiteExportLink to record
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