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Zero Estimation Cost Strategy for Witsenhausen Counterexample with Causal Encoder
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
Univ. Rennes, CNRS, Inria, IRISA UMR 6074, Rennes, France.ORCID iD: 0000-0002-6806-8592
2025 (English)In: ISIT 2025 - 2025 IEEE International Symposium on Information Theory, Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2025Conference paper, Published paper (Refereed)
Abstract [en]

We propose a zero estimation cost (ZEC) scheme for causal-encoding noncausal-decoding vector-valued Witsenhausen counterexample based on the coordination coding result. In contrast to source coding, our goal is to communicate a controlled system state. The introduced ZEC scheme is a joint controlcommunication approach that transforms the system state into a sequence that can be efficiently communicated using block coding. The noncausal decoder receives sufficient information for reconstructing the system state perfectly, enabling the achievable estimation cost to be zero. Numerical results show that our approach significantly reduces the power budget required for achieving zero-estimation-cost state reconstruction at the decoder. In the second part, we introduce a more general non-zero estimation cost (Non-ZEC) scheme. We observe numerically that the Non-ZEC scheme operates as a time-sharing mechanism between Witsenhausen's original two-point strategy and the ZEC scheme. Overall, by leveraging block-coding gain, our proposed methods substantially improve the power-estimation trade-off for Witsenhausen counterexample.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2025.
National Category
Telecommunications Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-373351DOI: 10.1109/ISIT63088.2025.11195293Scopus ID: 2-s2.0-105021985589OAI: oai:DiVA.org:kth-373351DiVA, id: diva2:2018164
Conference
2025 IEEE International Symposium on Information Theory, ISIT 2025, Ann Arbor, United States of America, June 22-27, 2025
Note

Part of ISBN 9798331543990

QC 20251202

Available from: 2025-12-02 Created: 2025-12-02 Last updated: 2025-12-02Bibliographically approved

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Zhao, MengyuanOechtering, Tobias J.

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