In many cases of practical interest, the angle of arrival distribution at the receiver is sufficiently narrow that one can use knowledge of the mean received signal levels, Ricean K-factors and the cross-correlation coefficient that characterize fading signals observed on orthogonally polarized diversity branches to predict the first-order statistics of polarization state dispersion. This allows one to use simple power-only measurements of narrowband polarization diversity to predict the performance of alternative polarization diversity schemes or polarization adaptive antennas in realistic environments. Moreover, our results offer a useful geometric interpretation of how decorrelation between polarization diversity branches arises: As the angular spread of the polarization state distribution on the Poincaré sphere broadens, the correlation between branches decreases. However, knowledge of the angular spread alone is not sufficient to predict the cross-correlation coefficient; the Ricean K-factors on the branches must be known as well. Otherwise, the analogy to a similar relationship between the angle of arrival distribution and correlation between branches in space diversity is striking.
Knowledge of the Ricean K-factor is important for accurate assessment and simulation of wireless channels, particularly those in which both terminals are stationary and a strong line-of-sight or fixed component exists. While previous work has focused on development of novel K-factor estimators, the practical matter of determining the minimum number of samples N (or minimum sample duration T) required to estimate K to within a specified confidence interval σ Κ has not received much attention. Here, for the case of a simple moment-based Kfactor estimator, we use Monte Carlo simulations to show how N and T depend upon the value of K, the confidence interval σ Κ , the correlation between successive samples, and the sampling interval ∆T. The results will be useful to those who use such an estimator in practical systems for the purposes of channel state estimation and/or radio resource management.
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