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Optimal Energy Allocation in Multisensor Estimation Over Wireless Channels Using Energy Harvesting and Sharing
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0002-4413-4225
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0003-0762-5743
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.ORCID iD: 0000-0001-9066-5468
Paderborn Univ, Dept Elect Engn EIM E, D-33098 Paderborn, Germany.ORCID iD: 0000-0002-4804-5481
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 10, p. 4337-4344Article in journal (Refereed) Published
Abstract [en]

We investigate the optimal power control for multisensor estimation of correlated random Gaussian sources. A group of wireless sensors obtains local measurements and transmits them to a remote fusion center (FC), which reconstructs the measurements using the minimum mean-square error estimator. All the sensors are equipped with an energy harvesting module and a transceiver unit for wireless, directed energy sharing between neighboring sensors. The sensor batteries are of finite storage capacity and prone to energy leakage. Our aim is to find optimal power control strategies, which determine the energies used to transmit data to the FC and shared between sensors, so as to minimize the long-term average distortion over an infinite horizon. We assume centralized causal information of the harvested energies and channel gains, which are generated by independent finite-state stationary Markov chains. The optimal power control policy is derived using a stochastic predictive control formulation. We also investigate the structure of the optimal solution, a Q-learning based sub-optimal power control scheme and two computationally simple and easy-to-implement heuristic policies. Extensive numerical simulations illustrate the performance of the considered policies.

Place, publisher, year, edition, pages
2019. Vol. 64, no 10, p. 4337-4344
Keywords [en]
Energy harvesting, energy sharing, fading, multisensor estimation, networks, power control, Q-learning
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:uu:diva-396738DOI: 10.1109/TAC.2019.2896048ISI: 000490772500037OAI: oai:DiVA.org:uu-396738DiVA, id: diva2:1373209
Funder
Swedish Research Council, 2017-04053Swedish Research Council, 2017-04186Available from: 2019-11-26 Created: 2019-11-26 Last updated: 2019-11-26Bibliographically approved

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Knorn, SteffiDey, SubhrakantiAhlén, Anders

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