Estimation-Energy Tradeoff for Scalar Gauss-Markov Signals With Kalman Filtering

In this letter, we investigate a receiver architecture, which uses the received signal in order to simultaneously harvest energy and estimate a Gauss-Markov linear process. We study three communication scenarios: i) static channel, ii) Rayleigh block-fading channel, and iii) high power amplifier (HPA) nonlinearities at the transmitter side. Theoretical results for the minimum mean square error as well as the average harvested energy are given for all cases and the fundamental tradeoff between estimation quality and harvested energy is characterized. We show that channel fading improves the estimation performance while HPA requires an extended Kalman filter at the receiver and significantly affects both the estimation and the harvesting efficiency.

Automated summary

This letter investigates a receiver architecture that performs energy harvesting and estimation of a Gauss-Markov linear process simultaneously using the received signal. We analyze three communication scenarios: static channel, Rayleigh block-fading channel, and high power amplifier (HPA) nonlinearities at the transmitter side. Theoretical results for minimum mean square error and average harvested energy are provided, and the tradeoff between estimation quality and harvested energy is characterized. It is shown that channel fading improves estimation performance, while HPA requires an extended Kalman filter and significantly affects both estimation and harvesting efficiency.