Estimating the Effective Sample Size to Select Independent Measurements in a Reverberation Chamber

Abstract

In reverberation chambers (RCs), measurements are usually performed by changing the boundary conditions using a mode stirrer. The major difficulty is to select uncorrelated samples in order to make a statistical analysis of the data. Furthermore, the knowledge of the number of independent samples is of crucial importance to assess the measurement accuracy. To evaluate whether measured data are independent, the conventional method compares the autocorrelation function (ACF) with the critical value 0.37. However, this criterion is generally not appropriate because the ACF probability density function (pdf) depends strongly on the sample size. For a measurement series of length N, the effective sample size (ESS) is defined as the number N' < N of independent samples, which would provide the same information as the N-size sample. This paper aims to provide a new method based on autoregressive (AR) models and the central limit theorem (CLT) in the case of dependent data, for estimating the ESS. The proposed method is easy to implement since it requires only the knowledge of simple statistical parameters. Moreover, it provides useful guidelines to assess the maximum number of independent samples available with the mode stirrer. Experimental results are in good agreement with the theoretical models, either for the electric field or the received power.