外观
论文arXiv 2610.2226513 天前
在基于拉取的马尔可夫源远程估计中最小化错误信息年龄
原文链接：Minimizing Age of Incorrect Information in Pull-Based Remote Estimation of a Markov Source（arXiv 2610.22265 · 发布日期：2026-10-15）
Minimizing Age of Incorrect Information in Pull-Based Remote Estimation of a Markov Source
研究基于拉取的DTMC源远程估计，用SMDP、belief-MDP和CDRL在ZD与UD信道模型下最小化MAoII。
核心要点
- 研究监视器按需拉取有限状态DTMC源时，如何在采样率约束下降低MAoII。
- 零时延信道下构建SMDP，以求解依赖观测的最优拉取策略。
- 单位时延使监视器无法完全获知AoII过程，因此引入由观测历史得到的belief。
- 为UD模型给出MAoII和平均采样率数值计算，并提出belief-MDP与CDRL策略。
验证范围与局限：验证范围限于有限状态不可约DTMC源以及零时延、单位时延信道模型。
英文摘要 (Abstract)
In this paper, we consider a discrete-time pull-based remote estimation system involving an information source and a remote monitor where the information source is a finite-state irreducible discrete-time Markov chain (DTMC) process. In this system, the remote monitor sends a pull request towards the source which responds by sampling its process so as to transmit the sampled values towards the monitor through a delay channel, encapsulated in information packets. We consider i) zero delay (ZD) and ii) unit delay (UD) channels, where the packet transmission times are zero and one slot, respectively. For remote estimation, we use the maximum a posteriori (MAP) estimator, and for quantifying its performance, we use the mean age of incorrect information (MAoII) which is a recently proposed freshness and mismatch metric that penalizes an incorrect estimation along with its duration. Our goal is to obtain pull policies so as to minimize MAoII under a long-run sampling rate constraint. For the ZD model, we propose a semi-Markov decision process (SMDP) formulation of the problem, which allows us to find the optimum observation-dependent pull policy. On the other hand, the UD channel prevents the monitor from possessing perfect information on the actual AoII process at any given time, and the SMDP formulation of the ZD channel cannot readily be used for the UD model. For the system with the UD channel, we first provide a numerical algorithm to calculate the MAoII and the average sampling rate for a given observation-dependent pull policy. For the optimization of the underlying remote estimation system, we use the joint distribution of the age and source processes to be obtained from the history of all observations, termed as belief. Subsequently, we propose a belief-dependent policy obtained via the belief-MDP formulation and a constrained deep reinforcement learning (CDRL) algorithm.
作者：Ismail Cosandal, Sennur Ulukus, Nail Akar
来源
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