Asymptotically Optimal Joint Sampling and Compression for Timely Status Updates: Age–Distortion Tradeoff

We consider a joint sampling and compression system for timely status updates. Samples are taken, quantized and encoded into binary sequences, which are sent to the destination. According to the timeliness requirement represented by an age penalty function at the destination, we formulate an optimization problem to jointly design sampler, quantizer and encoder, optimizing data freshness on the basis of satisfying a mean-squared error (MSE) distortion constraint of the samples. We use average nonlinear age (ANA) to measure data freshness and study linear, squared and exponential penalty functions. We prove that the zero-wait sampling, the uniform quantization, and the ANA-optimal coding policies together provide an asymptotically optimal solution, in the sense that, as the average distortion approaches zero, the solution achieves the minimum ANA asymptotically, for the linear and squared penalty functions. When the probability density function (pdf) is weakly unimodal, we also prove that the zero-wait sampling, the K-L quantization which is asymptotically optimal for the quantization with Rényi entropy constraint, and the ANA-optimal coding policies are asymptotically optimal for the exponential penalty function. Furthermore, we prove that the optimal ANA is asymptotically linear with respect to the log MSE distortion with a slope of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\frac{3}{4}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\sqrt{\frac{7}{12}}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-1$</tex-math></inline-formula>, corresponding to the linear, squared and exponential penalty functions, respectively.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-10-14
DOI
https://doi.org/10.1109/tvt.2024.3479242
Citations
4
Primary Topic
Age of Information Optimization
Type
article
Field-Weighted Citation Impact
1.40

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article

Asymptotically Optimal Joint Sampling and Compression for Timely Status Updates: Age–Distortion Tradeoff

Wenyi Zhang, Jun Li
4 citations
IEEE Transactions on Vehicular Technology
Age of Information Optimization
1.40
article

Asymptotically Optimal Joint Sampling and Compression for Timely Status Updates: Age–Distortion Tradeoff

Wenyi Zhang, Jun Li
article en
4 citations

Abstract

We consider a joint sampling and compression system for timely status updates. Samples are taken, quantized and encoded into binary sequences, which are sent to the destination. According to the timeliness requirement represented by an age penalty function at the destination, we formulate an optimization problem to jointly design sampler, quantizer and encoder, optimizing data freshness on the basis of satisfying a mean-squared error (MSE) distortion constraint of the samples. We use average nonlinear age (ANA) to measure data freshness and study linear, squared and exponential penalty functions. We prove that the zero-wait sampling, the uniform quantization, and the ANA-optimal coding policies together provide an asymptotically optimal solution, in the sense that, as the average distortion approaches zero, the solution achieves the minimum ANA asymptotically, for the linear and squared penalty functions. When the probability density function (pdf) is weakly unimodal, we also prove that the zero-wait sampling, the K-L quantization which is asymptotically optimal for the quantization with Rényi entropy constraint, and the ANA-optimal coding policies are asymptotically optimal for the exponential penalty function. Furthermore, we prove that the optimal ANA is asymptotically linear with respect to the log MSE distortion with a slope of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\frac{3}{4}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-\sqrt{\frac{7}{12}}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-1$</tex-math></inline-formula>, corresponding to the linear, squared and exponential penalty functions, respectively.

IEEE Transactions on Vehicular TechnologyVol. 74(2)
University of Science and Technology of China (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 17%
Age of Information Optimization
1.40
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