City Research Online

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.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising.
Publisher Keywords:	Costs, Numerical analysis, Estimation, Channel estimation, Transforms, Markov processes, Nash equilibrium, Approximation algorithms, Monitoring, Optimization
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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