Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910468373741568 |
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| author | Luo, Jiping Pappas, Nikolaos |
| author_facet | Luo, Jiping Pappas, Nikolaos |
| contents | This paper investigates goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. An agent decides the updating times of the sources and transmits the packet to a remote destination over an unreliable channel with delay. The destination is tasked with source reconstruction for actuation. We utilize the metric \textit{cost of actuation error} (CAE) to capture the state-dependent actuation costs. We aim for a sampling policy that minimizes the long-term average CAE subject to an average resource constraint. We formulate this problem as an average-cost constrained Markov Decision Process (CMDP) and relax it into an unconstrained problem by utilizing \textit{Lyapunov drift} techniques. Then, we propose a low-complexity \textit{drift-plus-penalty} (DPP) policy for systems with known source/channel statistics and a Lyapunov optimization-based deep reinforcement learning (LO-DRL) policy for unknown environments. Our policies significantly reduce the number of uninformative transmissions by exploiting the timing of the important information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07346 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems Luo, Jiping Pappas, Nikolaos Systems and Control Information Theory Networking and Internet Architecture This paper investigates goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. An agent decides the updating times of the sources and transmits the packet to a remote destination over an unreliable channel with delay. The destination is tasked with source reconstruction for actuation. We utilize the metric \textit{cost of actuation error} (CAE) to capture the state-dependent actuation costs. We aim for a sampling policy that minimizes the long-term average CAE subject to an average resource constraint. We formulate this problem as an average-cost constrained Markov Decision Process (CMDP) and relax it into an unconstrained problem by utilizing \textit{Lyapunov drift} techniques. Then, we propose a low-complexity \textit{drift-plus-penalty} (DPP) policy for systems with known source/channel statistics and a Lyapunov optimization-based deep reinforcement learning (LO-DRL) policy for unknown environments. Our policies significantly reduce the number of uninformative transmissions by exploiting the timing of the important information. |
| title | Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems |
| topic | Systems and Control Information Theory Networking and Internet Architecture |
| url | https://arxiv.org/abs/2311.07346 |