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

TL;DR

This paper explores the tradeoff between energy harvesting and estimation accuracy for scalar Gauss-Markov signals using Kalman filtering across different communication scenarios, revealing how channel conditions and transmitter nonlinearities impact performance.

Contribution

It provides a theoretical analysis of the energy-estimation tradeoff in various channel conditions and transmitter nonlinearities, introducing new insights into optimal receiver design.

Findings

Channel fading enhances estimation performance.

HPA nonlinearities require extended Kalman filtering.

Tradeoff between estimation quality and harvested energy is characterized.

Abstract

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.