Core Concepts
Optimizing remote estimation of multiple Markov sources under constraints through semantic-aware communication.
Abstract
This paper explores semantic-aware communication for remote estimation of multiple Markov sources over a lossy and rate-constrained channel. It introduces an optimal scheduling policy to minimize long-term state-dependent costs of estimation errors. The study leverages Lagrangian dynamic programming and proposes novel algorithms for policy search and online scheduling to address computational challenges. Results indicate the efficiency of semantic-aware policies in achieving optimal outcomes by strategically utilizing information timing.
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Introduction
- Remote estimation challenges in networked control systems.
- Need for efficient communication protocols.
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Semantic-Aware Communication
- Importance of semantics in information flow.
- Consideration of different tolerances for estimation errors.
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Information Freshness Metrics
- Age of Information (AoI) vs. Value of Information (VoI).
- Introduction of new metrics like Age of Incorrect Information (AoII) and Urgency of Information (UoI).
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Cost Metrics
- Cost of Actuation Error (CAE) based on state-dependent actuation costs.
- Semantic-empowered metrics for prioritizing information flow efficiently.
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System Model
- Description of sources, agent, channel, receiver, and cost functions.
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CMDP Formulation
- Problem formulation as an average-cost constrained Markov Decision Process.
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Lagrangian MDP
- Derivation and analysis of Lagrangian MDP with multiplier λ.
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Optimal Policy Structure
- Existence and structure of constrained optimal policies based on Lagrangian techniques.
Stats
Unlike most existing studies that treat all source states equally, this study exploits the semantics of information to consider different tolerances for estimation errors.
Numerical results show that continuous transmission is inefficient, emphasizing the importance of strategic utilization of information timing.