Transmission Strategies for Remote Estimation with an Energy Harvesting Sensor
Journal article, 2017

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.

energy harvesting

estimation

Distortion minimization

mean-square error

wireless sensor networks

Author

Ayca Ozcelikkale

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Mats Viberg

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Wireless Communications

1536-1276 (ISSN)

Vol. 16 7 4390-4403 7917371

Areas of Advance

Information and Communication Technology

Energy

Subject Categories

Communication Systems

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

DOI

10.1109/TWC.2017.2698030