A Stochastic Framework for the Variability Analysis of Textile Antennas

Abstract

Abstract-A novel framework to accurately quantify the effect of stochastic variations of design parameters on the performance of textile antennas is developed and tested. First, a sensitivity analysis is applied to get a rough idea about the effect of these random variations on the textile antenna's performance. Next, a more detailed view is obtained by a Generalized Polynomial Chaos technique that accurately quantifies the statistical distribution of the textile antenna's figures of merit, for a given range over which geometry and material parameters vary statistically according to a given distribution. The method is validated both for a simple inset-fed patch textile microstrip antenna and for a dual-polarized textile antenna. For the latter, the probability density function corresponding to its most sensitive design parameter, being the width, is experimentally estimated by means of measurements performed on 100 patches. A Kolmogorov-Smirnoff test proves that, for all considered examples, the results are as accurate as those obtained via Monte Carlo analysis, while the new technique is much more efficient. Indeed, speedups up to a factor 1667 are demonstrated.

Generalized polynomial chaos paradigms to model uncertainty in wireless links

“…More specifically, such antenna will be subject to uncertainties in its geometrical dimensions as well as to variations in the relative permittivity r of the protective foam applied as a substrate. According to [1], the most significant variations in radiation impedance Z RX = Z re + jZ im and radiation pattern are due to random changes in the patch length L, the patch width W and relative permittivity of the substrate. Hence, the following gPC expansions are put forward: …”

“…These are leveraged for a fast evaluation of the radiative near-field link, following the formalism adopted in [5], from which the wireless link efficiency η link and the incoming power P RX , can be computed. Given the experimental data found in [1], [11], L, W and r are all independent Gaussian random variables (see Table II for their mean values and standard deviations). Therefore, the multivariate polynomials φ (10)- (15) correspond to products of Hermite polynomials.…”

“…In particular, two main types of random variations affect the performance of a WPT system in which textile antennas are deployed. First, the antenna's radiation impedance undergoes random variations due to production uncertainties [1]. As a result, the receiving antenna and the embedded rectifying circuit may suffer from mismatch.…”

Stochastic modeling framework for wireless power transfer in the radiative near-field

“…The most significant design parameters L, W and r (Fig. 2 for more details) are found [11], [12] to vary as independent Gaussian random variables with standard deviations, σ L = 0.1268 mm, σ L = 0.1628 mm and σ r = 0.03190, respectively. Moreover, there is uncertainty on antenna positions d, x, y and orientation angles θ and φ.…”

Recent advances in materials and technologies for body-centric and IoT antenna systems

“…Instead, they need to rely on dedicated characterization procedures to characterize the dielectric characteristics and the losses of each potential substrate material [7], [8]. Second, they must account for large variations in material characteristics and substrate inhomogeneity [9]. Capturing these variability and uncertainty aspects calls for the inclusion of stochastic design frameworks in full-wave computer-aided design process.…”