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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.06749 |
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| _version_ | 1866912230172262400 |
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| author | Vilni, Saeid Sadeghi Zakeri, Abolfazl Moltafet, Mohammad Codreanu, Marian |
| author_facet | Vilni, Saeid Sadeghi Zakeri, Abolfazl Moltafet, Mohammad Codreanu, Marian |
| contents | We consider a status update system consisting of a finite-state Markov source, an energy-harvesting-enabled transmitter, and a sink. The forward and feedback channels between the transmitter and the sink are error-prone. We study the problem of minimizing the long-term time average of a (generic) distortion function subject to an energy causality constraint. Since the feedback channel is error-prone, the transmitter has only partial knowledge about the transmission results and, consequently, about the estimate of the source state at the sink. Therefore, we model the problem as a partially observable Markov decision process (POMDP), which is then cast as a belief-MDP problem. The infinite belief space makes solving the belief-MDP difficult. Thus, by exploiting a specific property of the belief evolution, we truncate the state space and formulate a finite-state MDP problem, which is then solved using the relative value iteration algorithm (RVIA). Furthermore, we propose a low-complexity transmission policy in which the belief-MDP problem is transformed into a sequence of per-slot optimization problems. Simulation results show the effectiveness of the proposed policies and their superiority compared to a baseline policy. Moreover, we numerically show that the proposed policies have switching-type structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time Tracking in a Status Update System with an Imperfect Feedback Channel Vilni, Saeid Sadeghi Zakeri, Abolfazl Moltafet, Mohammad Codreanu, Marian Systems and Control We consider a status update system consisting of a finite-state Markov source, an energy-harvesting-enabled transmitter, and a sink. The forward and feedback channels between the transmitter and the sink are error-prone. We study the problem of minimizing the long-term time average of a (generic) distortion function subject to an energy causality constraint. Since the feedback channel is error-prone, the transmitter has only partial knowledge about the transmission results and, consequently, about the estimate of the source state at the sink. Therefore, we model the problem as a partially observable Markov decision process (POMDP), which is then cast as a belief-MDP problem. The infinite belief space makes solving the belief-MDP difficult. Thus, by exploiting a specific property of the belief evolution, we truncate the state space and formulate a finite-state MDP problem, which is then solved using the relative value iteration algorithm (RVIA). Furthermore, we propose a low-complexity transmission policy in which the belief-MDP problem is transformed into a sequence of per-slot optimization problems. Simulation results show the effectiveness of the proposed policies and their superiority compared to a baseline policy. Moreover, we numerically show that the proposed policies have switching-type structures. |
| title | Real-time Tracking in a Status Update System with an Imperfect Feedback Channel |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.06749 |