Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and Astrophysic

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Energy spectra in turbulent bubbly flows

We conduct experiments in a turbulent bubbly flow to study the nature of the transition between the classical $-$5/3 energy spectrum scaling for a single-phase turbulent flow and the $-$3 scaling for a swarm of bubbles rising in a quiescent liquid and of bubble-dominated turbulence. The bubblance parameter, which measures the ratio of the bubble-induced kinetic energy to the kinetic energy induced by the turbulent liquid fluctuations before bubble injection, is often used to characterise the bubbly flow. We vary the bubblance parameter from $b = \infty$ (pseudo-turbulence) to $b = 0$ (single-phase flow) over 2-3 orders of magnitude ($0.01 - 5$) to study its effect on the turbulent energy spectrum and liquid velocity fluctuations. The probability density functions (PDFs) of the liquid velocity fluctuations show deviations from the Gaussian profile for $b > 0$, i.e. when bubbles are present in the system. The PDFs are asymmetric with higher probability in the positive tails. The energy spectra are found to follow the $-$3 scaling at length scales smaller than the size of the bubbles for bubbly flows. This $-$3 spectrum scaling holds not only in the well-established case of pseudo-turbulence, but surprisingly in all cases where bubbles are present in the system ($b > 0$). Therefore, it is a generic feature of turbulent bubbly flows, and the bubblance parameter is probably not a suitable parameter to characterise the energy spectrum in bubbly turbulent flows. The physical reason is that the energy input by the bubbles passes over only to higher wave numbers, and the energy production due to the bubbles can be directly balanced by the viscous dissipation in the bubble wakes as suggested by Lance $\&$ Bataille (1991). In addition, we provide an alternative explanation by balancing the energy production of the bubbles with viscous dissipation in the Fourier space.Comment: J. Fluid Mech. (in press

Galaxy Clustering and Large-Scale Structure from z = 0.2 to z = 0.5 in Two Norris Redshift Surveys

(abridged) We present a study of the nature and evolution of large-scale structure based on two independent redshift surveys of faint field galaxies conducted with the 176-fiber Norris Spectrograph on the Palomar 200-inch telescope. The two surveys together sparsely cover ~20 sq. degrees and contain 835 r < 21 mag galaxies with redshifts 0.2 < z < 0.5. Both surveys have a median redshift of z = 0.30. In order to obtain a rough estimate of the cosmic variance, we analyze the two surveys independently. We measure the comoving correlation length to be 3.70 +/- 0.13 h^-1 Mpc at z = 0.30 with a power-law slope gamma = 1.77 +/- 0.05. Dividing the sample into low (0.2 < z < 0.3) and high (0.32 < z < 0.5) redshift intervals, we see no evidence for a change in the comoving correlation length over the redshift range 0.2 < z < 0.5. Similar to the well-established results in the local universe, we find that intrinsically bright galaxies are more strongly clustered than intrinsically faint galaxies and that galaxies with little ongoing star formation, as judged from the rest-frame equivalent width of the [OII]3727, are more strongly clustered than galaxies with significant ongoing star formation. The rest-frame pairwise velocity dispersion of the sample is 326^+67_-52 km s^-1, ~25% lower than typical values measured locally. The appearance of the galaxy distribution, particularly in the more densely sampled Abell 104 field, is quite striking. The pattern of sheets and voids which has been observed locally continues at least to z ~ 0.5. A friends-of-friends analysis of the galaxy distribution supports the visual impression that > 90% of all galaxies at z < 0.5 are part of larger structures with overdensities of > 5.Comment: 40 pages including 26 Postscript figures; revised version to match version accepted by Ap

Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author