# Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems

arXiv: https://arxiv.org/abs/2311.07346 · HTML: https://mlsanity.com/paper/2311.07346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints](https://mlsanity.com/paper/2403.16855/) (2024)
- [Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion](https://mlsanity.com/paper/2503.17777/) (2025)

## Abstract (arXiv)

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 textitcost 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 textitLyapunov drift techniques. Then, we propose a low-complexity textitdrift-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.