Issues in the Estimation of Ricean K-Factor from Correlated Samples

Abstract

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.