Causal Vector-Valued Witsenhausen Counterexamples with Feedback

We study the continuous vector-valued Witsen-hausen counterexample with Gaussian states through the lens of empirical coordination coding. We characterize the region of achievable pairs of costs in three scenarios: (i) causal encoding and causal decoding, (ii) causal encoding and causal decoding with channel feedback, and (iii) causal encoding and noncausal decoding with channel feedback. In these vector-valued versions of the problem, the optimal coding schemes must rely on a time-sharing strategy, since the region of achievable pairs of costs might not be convex in the scalar version of the problem. We examine the role of the channel feedback when the encoder is causal and the decoder is either causal or non-causal, and we show that feedback improves the performance, only when the decoder is non-causal.

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Published
2024-11-24
DOI
https://doi.org/10.1109/itw61385.2024.10806960
Citations
2
Primary Topic
Computability, Logic, AI Algorithms
Type
article
Field-Weighted Citation Impact
0.72

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article

Causal Vector-Valued Witsenhausen Counterexamples with Feedback

Maël Le Treust, Tobias J. Oechtering, Mengyuan Zhao
2 citations
Computability, Logic, AI Algorithms
0.72
article

Causal Vector-Valued Witsenhausen Counterexamples with Feedback

Maël Le Treust, Tobias J. Oechtering, Mengyuan Zhao
article en
2 citations

Abstract

We study the continuous vector-valued Witsen-hausen counterexample with Gaussian states through the lens of empirical coordination coding. We characterize the region of achievable pairs of costs in three scenarios: (i) causal encoding and causal decoding, (ii) causal encoding and causal decoding with channel feedback, and (iii) causal encoding and noncausal decoding with channel feedback. In these vector-valued versions of the problem, the optimal coding schemes must rely on a time-sharing strategy, since the region of achievable pairs of costs might not be convex in the scalar version of the problem. We examine the role of the channel feedback when the encoder is causal and the decoder is either causal or non-causal, and we show that feedback improves the performance, only when the decoder is non-causal.

Centre National de la Recherche Scientifique (FR), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), Université de Rennes (FR), KTH Royal Institute of Technology (SE)
Vetenskapsrådet
Openalex Percentile: Top 24%
Computability, Logic, AI Algorithms
0.72
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Causal Vector-Valued Witsenhausen Counterexamples with Feedback — Maël Le Treust, Tobias J. Oechtering, et al. · (2024) | TGRS Research Map | TGRS