ANTENNA IMPEDANCE ESTIMATION IN CORRELATED RAYLEIGH FADING CHANNELS

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 a scalar optimization. We explore the performance, e.g., efficiency against Cramer-Rao lower bounds, of these estimators through numerical examples. The impact of channel correlation on impedance estimation accuracy is also investigated.