Sample Selection Algorithms for Enhanced MIMO Antenna Measurements Using Mode-Stirred Reverberation Chambers

We formulate and perform principal component analysis (PCA) of mechanically stirred fields, based on tuner sweep data collected across a frequency band at a single location of a sensor inside a reverberation chamber. Both covariance-and correlationbased PCA in undermoded and overmoded regime are performed and intercompared. The nonstationarity of the stir performance as a function of the angular position of the stirrer is demonstrated. It is shown that this nonuniformity can be quantified and exploited to select a set of optimal stir angles. The rotated principal components are found to be interpretable as energy stirred by specific angular sectors of the stirrer and are related to the correlation structure of the data. The analysis leads to the concept of eigenstirrings (stir modes), which form an orthonormal set of empirical basis functions for expanding stir data.

Section: B Selection Criterion For Number Of Pcs To Be Extractedmentioning

confidence: 76%

Optimizing Low-Frequency Mode Stirring Performance Using Principal Component Analysis

Arnaut¹

2014

“…They are widely used to carry out electromagnetic compatibility (EMC) compliance testing for both immunity and emission [3]. Other EMC applications are in the field of shielding effectiveness measurement [4], material absorbing properties characterizations [5], [6], the measurement of the diversity gain [7], and the total efficiency [8], [9]. Recently, it was demonstrated the ability of this EM environment to replicate multipath propagation [10] so making them able to test wireless communication devices [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Power Boosting and Compensation During OTA Testing of a Real 4G LTE Base Station in Reverberation Chamber

Micheli

Barazzetta

Moglie

et al. 2015

A fully operational 4G long-term evolution (LTE) base station is tested in a reverberation chamber to analyze its performance in the presence of a multipath environment, typical of wireless and vehicular communications. Transmission quality parameters are measured ranging from the empty chamber situation (very rich multipath channel) to a very high loading condition to mitigate multipath. In that way, both outdoor and indoor propagation are accounted for. A large attenuation is inserted between the transmitter and the antenna to reduce the signal received by the user to real-life values encountered in both outdoor and indoor environments. In these scenarios, operators may choose to transmit a constant power spectral density throughout all LTE spectrum, or to increase the energy of control channels at the expense of data channels in order to enforce transmission and provide better quality to the user, especially in poor radio conditions. This is called “power boosting,” and its effect is analyzed in this paper

“…, [4], [5], the study of intentional electromagnetic interference (EMI) to electronics housed inside metallic enclosures [6], [7], [8], [9], [10], [11], [12], and the analysis of channel properties (coherent bandwidth, spatial correlation, power decay profile) in the indoor wireless over-the-air (OTA) testing [13], [14], [15], [16].…”

mentioning

confidence: 99%

Statistical Analysis of Cavity Quality Factor Due to Localized Losses With the Stochastic Green's Function Method

Lin,

Luo,

Shao

et al. 2024

The statistical characterization of the cavity quality factor (Q-factor) holds significant practical importance, especially in the context of large and complex metallic enclosures. While there are existing methods for analyzing Q-factor statistics attributed to distributed and uniform losses, there is a noticeable gap in addressing the statistical cavity Q-factor arising from localized losses, such as aperture leakage, wall coating, and absorptive loading. This article introduces a physics-oriented, hybrid deterministic-stochastic approach to predict the statistical distribution of cavity Q-factor due to localized losses. The key ingredient of this method is the stochastic Green's function integral equation formulation, grounded in a statistical description of the cavity eigenmodes within an enclosed electromagnetic environment. The computational model is evaluated through both numerical and laboratory experiments, validating its reliability and applicability in real-world scenarios.