Remote estimation of Markov processes over costly channels: on implicit information benefits
File(s)
Accepted version
Author(s)
Santi, Edoardo D
Soleymani, Touraj
Gündüz, Deniz
Type
Conference Paper
Abstract
In this paper, we study the remote estimation of discrete-state Markov processes over costly point-to-point channels. We formulate this problem as an infinite-horizon optimization problem with two players, i.e., a sensor and a monitor, that have distinct information, and with a reward function that takes into account both the communication cost and the estimation quality. We show that the main challenge in solving this problem is associated with the consideration of implicit information, i.e., information that the monitor can obtain about the source when the sensor is idle. Our main objective is to develop a framework for finding exact or approximate solutions to this problem without neglecting implicit information a priori. To that end, we propose three different algorithms, and discuss their properties. The first one is an alternating optimization algorithm that converges to a Nash equilibrium. The second one optimizes both players’ policies jointly, and is guaranteed to find a globally optimal solution. The last one is a heuristic algorithm that can find a near-optimal solution. Finally, we compare the performance of these algorithms through a numerical analysis.
Date Issued
2025-03-11
Date Acceptance
2025-12-01
Citation
GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 2025, pp.1353-1358
ISSN
2334-0983
Publisher
IEEE
Start Page
1353
End Page
1358
Journal / Book Title
GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Copyright Statement
© 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Publication Status
Published
Start Date
2024-12-08
Finish Date
2024-12-12
Coverage Spatial
Cape Town, South Africa