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Transmission Strategies for Remote Estimation with an Energy Harvesting Sensor

Ayca Ozcelikkale (Institutionen för signaler och system, Signalbehandling) ; Tomas McKelvey (Institutionen för signaler och system, Signalbehandling) ; Mats Viberg (Institutionen för signaler och system, Signalbehandling)
IEEE Transactions on Wireless Communications (1536-1276). Vol. 16 (2017), 7, p. 4390-4403.
[Artikel, refereegranskad vetenskaplig]

We consider the remote estimation of a time-correlated signal using an energy harvesting (EH) sensor. The sensor observes the unknown signal and communicates its observations to a remote fusion center using an amplify-and-forward strategy. We consider the design of optimal power allocation strategies in order to minimize the mean-square error at the fusion center. Contrary to the traditional approaches, the degree of correlation between the signal values constitutes an important aspect of our formulation. We provide the optimal power allocation strategies for a number of illustrative scenarios. We show that the most majorized power allocation strategy, i.e. the power allocation as balanced as possible, is optimal for the cases of circularly wide-sense stationary (c.w.s.s.) signals with a static correlation coefficient, and sampled low-pass c.w.s.s. signals for a static channel. We show that the optimal strategy can be characterized as a water-filling type solution for sampled low-pass c.w.s.s. signals for a fading channel. Motivated by the high-complexity of the numerical solution of the optimization problem, we propose low-complexity policies for the general scenario. Numerical evaluations illustrate the close performance of these low-complexity policies to that of the optimal policies, and demonstrate the effect of the EH constraints and the degree of freedom of the signal.

Nyckelord: Distortion minimization, estimation, mean-square error, energy harvesting, wireless sensor networks

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Denna post skapades 2017-04-23. Senast ändrad 2017-08-16.
CPL Pubid: 248932


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Institutionen för signaler och system, Signalbehandling (1900-2017)


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