Semantic-aware Real-time Tracking of a Markov Source under Sampling and Transmission Costs
Zakeri, Abolfazl; Moltafet, Mohammad; Codreanu, Marian (2024-04-01)
IEEE
01.04.2024
A. Zakeri, M. Moltafet and M. Codreanu, "Semantic-aware Real-time Tracking of a Markov Source under Sampling and Transmission Costs," 2023 57th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2023, pp. 694-698, doi: 10.1109/IEEECONF59524.2023.10476949
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406114357
https://urn.fi/URN:NBN:fi:oulu-202406114357
Tiivistelmä
Abstract
We address the real-time tracking problem of a partially observable Markov source under sampling and trans-mission costs in an energy harvesting system with an unreliable communication channel. We provide a semantic-aware optimal sampling and transmission policy that minimizes the average value of a general distortion subject to an energy causality constraint. We formulate a partially observable Markov decision process (POMDP) problem. To solve the problem, we cast it into a belief MDP problem. Subsequently, by effectively bounding the belief space, we formulate a finite-state MDP problem, which is solved using relative value iteration. The simulation results demonstrate the effectiveness of the derived policy and highlight the significant impact of the source dynamics on performance.
We address the real-time tracking problem of a partially observable Markov source under sampling and trans-mission costs in an energy harvesting system with an unreliable communication channel. We provide a semantic-aware optimal sampling and transmission policy that minimizes the average value of a general distortion subject to an energy causality constraint. We formulate a partially observable Markov decision process (POMDP) problem. To solve the problem, we cast it into a belief MDP problem. Subsequently, by effectively bounding the belief space, we formulate a finite-state MDP problem, which is solved using relative value iteration. The simulation results demonstrate the effectiveness of the derived policy and highlight the significant impact of the source dynamics on performance.
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