Polarization mode dispersion (PMD) field measurements on deployed buried fibres showed that the PMD variation over the 1520 to 1570 nm wavelength was stochastic. The PMD variation over the 98-hour period for each wavelength was directional and limited; they are due to the presence of random mode coupling along the fibre length and limited influence from extrinsic perturbations over time, respectively. PMD variation in the wavelength domain showed that the mean first-order PMD (FO-PMD) value is independent of whether the FO-PMD statistics of a fibre link approaches the Maxwellian theoretical distribution; the key factor is sufficient random mode coupling. The accompanying second-order PMD (SO-PMD) statistics, with FO-PMD statistics approaching Maxwellian, followed the PDF given by Foschini et al. (1999). The FO- and SO-PMD statistics at a given wavelength gave nonstochastic PMD distributions with time. 1. Introduction The polarization mode dispersion (PMD) in deployed fibres is known to evolve in a stochastic pattern due to unpredictable extrinsic perturbations (i.e., environmental changes, vibrations, and human interactions) with time and nonuniform intrinsic perturbations (i.e., core asymmetry and internal stress) experienced along the fibre length. The unpredictable variation of extrinsic and intrinsic perturbations with time and fibre length makes PMD statistics stochastic about some average value, either with wavelength or with time. This means that the PMD of optical systems with time and wavelength is unpredictable; therefore, one must resort to statistical analysis. The statistical property of PMD that attracted the initial interest was the mean first-order PMD (FO-PMD) [1, 2]. Thus, all PMD measurement techniques currently in use in the telecommunication industry require an averaging procedure in order to determine the overall PMD of a fibre link [2, 3]. Most, if not all, of thework done has been focused mainly on FO- and second-order PMD (SO-PMD) statistics, which is likely due to the FO- and SO-PMD vectors being known to be statistically dependent on each other [4, 5]. However, Phua and Haus [6] concluded that FO-PMD can be more accurately characterised than SO-PMD. Understanding the nature and characteristics of PMD is a key step towards the construction of effective PMD emulators and compensation techniques. The statistical characterisation of PMD includes the probability densities of FO- and SO-PMD, the scaling of the PMD phenomena with changes in the mean FO-PMD, various correlation functions, and characteristics associated with the
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