Antenna Impedance Estimation in Correlated Rayleigh Fading Channels

TL;DR

This paper develops maximum likelihood estimators for antenna impedance and channel variance in correlated Rayleigh fading channels, analyzing their efficiency and the impact of channel correlation on estimation accuracy.

Contribution

It introduces a novel ML estimation framework for antenna impedance in correlated fading channels, considering nuisance parameters and scalar optimization methods.

Findings

ML estimators are efficient relative to Cramer-Rao bounds.

Channel correlation significantly affects impedance estimation accuracy.

Numerical results demonstrate the estimators' performance under various conditions.

Abstract

We formulate antenna impedance estimation in a classical estimation framework under correlated Raleigh fading channels. Based on training sequences of multiple packets, we derive the ML estimators for antenna impedance and channel variance, treating the fading path gains as nuisance parameters. These ML estimators can be found via scalar optimization. We explore the efficiency of these estimators against Cramer-Rao lower bounds by numerical examples. The impact of channel correlation on impedance estimation accuracy is investigated.