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 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.

Impedance Variation Detection at MISO Receivers

Techniques have been proposed to estimate unknown antenna impedance due to time-varying near-field loading conditions at multiple-input single-output (MISO) receivers. However, it remains unclear when a change occurs and impedance estimation becomes necessary. In this letter, we address this problem by formulating it as a hypothesis test. Our contributions include deriving a generalized likelihood-ratio test (GLRT) detector to decide if the antenna impedance has changed over two groups of packets. This GLRT formulation leads to a novel optimization problem, but we propose a binary search based algorithm to solve it efficiently. Our derived GLRT detector enjoys a better detection and false alarm trade-off when compared with a well-known, reference detector in simulations. As one result, more transmit diversity significantly improves detection accuracy at a given false alarm rate, especially in slow fading channels.

Antenna Impedance Estimation at MIMO Receivers

This paper considers antenna impedance estimation based on training sequences at MIMO receivers. The goal is to firstly leverage extensive resources available in most wireless systems for channel estimation to estimate antenna impedance in real-time. We assume the receiver switches its impedance in a predetermined fashion during each training sequence. Based on voltage observation across the load, a classical estimation framework is developed incorporating the Rayleigh fading assumption. We then derive in closed-form a maximum-likelihood (ML) estimator under i.i.d. fading and show this same ML estimator is a method of moments (MM) estimator in correlated channels. Numerical results suggest a fast algorithm, i.e., MLE in i.i.d. fading and the MM estimator in correlated fading, that estimates the unknown antenna impedance in real-time for all Rayleigh fading channels.