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Timely Tracking of a Wiener Process With Single Bit Quantization

arXiv 2505.01419 ↗First posted 2 May 2025cs.IT eess.SP math.ITcited by 0

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Abstract (arXiv)

We consider the problem of timely tracking of a Wiener process via an energy-conserving sensor by utilizing a single bit quantization strategy under periodic sampling. Contrary to conventional single bit quantizers which only utilize the transmitted bit to convey information, in our codebook, we use an additional `emptyset' symbol to encode the event of not transmitting. Thus, our quantization functions are composed of three decision regions as opposed to the conventional two regions. First, we propose an optimum quantization method in which the optimum quantization functions are obtained by tracking the distributions of the quantization error. However, this method requires a high computational cost and might not be applicable for energy-conserving sensors. Thus, we propose two additional low complexity methods. In the last-bit aware method, three predefined quantization functions are available to both devices, and they switch the quantization function based on the last transmitted bit. With the Gaussian approximation method, we calculate a single quantization function by assuming that the quantization error can be approximated as Gaussian. While previous methods require a constant delay assumption, this method also works for random delay. We observe that all three methods perform similarly in terms of mean-squared error and transmission cost.

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References · 2

  1. 2020Age of Information: An Introduction and Surveycited by 70
  2. 2025Source Coding for a Wiener Processcited by 1

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  2. 2025A Non-Uniform Quantization Framework for Time-Encoding Machines
  3. 2024Multi-bit Distributed Detection of Sparse Stochastic Signals over Error-Prone Reporting Channels
  4. 2023The impact of frame quantization on the dynamic range of a one-bit image sensor
  5. 2023One-Bit Quadratic Compressed Sensing: From Sample Abundance to Linear Feasibility
  6. 2025Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things
  7. 2024Task-Based Quantizer Design for Sensing With Random Signals
  8. 2021Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering