Sampling of the Wiener Process for Remote Estimation Over a Channel With Random Delay

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Sun Y., Polyanskiy Y., UYSAL E.

IEEE TRANSACTIONS ON INFORMATION THEORY, vol.66, no.2, pp.1118-1135, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 66 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.1109/tit.2019.2937336
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, MathSciNet, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1118-1135
  • Keywords: Information age, Estimation error, Silicon, Real-time systems, Channel estimation, Servers, Sampling, remote estimation, age of information, Wiener process, queuing system, STATE ESTIMATION, COMMUNICATION COSTS, NOISE
  • Middle East Technical University Affiliated: Yes


In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator over a channel that is modeled as a queue. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We study the optimal online sampling strategy that minimizes the mean square estimation error subject to a sampling rate constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by how much the Wiener process varies during the random service time and the maximum allowed sampling rate. Further, if the sampling times are independent of the observed Wiener process, the above sampling problem for minimizing the estimation error is equivalent to a sampling problem for minimizing the age of information. This reveals an interesting connection between the age of information and remote estimation error. Our comparisons show that the estimation error achieved by the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and periodic sampling.