3,390 research outputs found
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Optical Frequency Comb Noise Characterization Using Machine Learning
A novel tool, based on Bayesian filtering framework and expectation
maximization algorithm, is numerically and experimentally demonstrated for
accurate frequency comb noise characterization. The tool is statistically
optimum in a mean-square-error-sense, works at wide range of SNRs and offers
more accurate noise estimation compared to conventional methods
A survey on fiber nonlinearity compensation for 400 Gbps and beyond optical communication systems
Optical communication systems represent the backbone of modern communication
networks. Since their deployment, different fiber technologies have been used
to deal with optical fiber impairments such as dispersion-shifted fibers and
dispersion-compensation fibers. In recent years, thanks to the introduction of
coherent detection based systems, fiber impairments can be mitigated using
digital signal processing (DSP) algorithms. Coherent systems are used in the
current 100 Gbps wavelength-division multiplexing (WDM) standard technology.
They allow the increase of spectral efficiency by using multi-level modulation
formats, and are combined with DSP techniques to combat the linear fiber
distortions. In addition to linear impairments, the next generation 400 Gbps/1
Tbps WDM systems are also more affected by the fiber nonlinearity due to the
Kerr effect. At high input power, the fiber nonlinear effects become more
important and their compensation is required to improve the transmission
performance. Several approaches have been proposed to deal with the fiber
nonlinearity. In this paper, after a brief description of the Kerr-induced
nonlinear effects, a survey on the fiber nonlinearity compensation (NLC)
techniques is provided. We focus on the well-known NLC techniques and discuss
their performance, as well as their implementation and complexity. An extension
of the inter-subcarrier nonlinear interference canceler approach is also
proposed. A performance evaluation of the well-known NLC techniques and the
proposed approach is provided in the context of Nyquist and super-Nyquist
superchannel systems.Comment: Accepted in the IEEE Communications Surveys and Tutorial
Continuous-variable optical quantum state tomography
This review covers latest developments in continuous-variable quantum-state
tomography of optical fields and photons, placing a special accent on its
practical aspects and applications in quantum information technology. Optical
homodyne tomography is reviewed as a method of reconstructing the state of
light in a given optical mode. A range of relevant practical topics are
discussed, such as state-reconstruction algorithms (with emphasis on the
maximum-likelihood technique), the technology of time-domain homodyne
detection, mode matching issues, and engineering of complex quantum states of
light. The paper also surveys quantum-state tomography for the transverse
spatial state (spatial mode) of the field in the special case of fields
containing precisely one photon.Comment: Finally, a revision! Comments to lvov(at)ucalgary.ca and
raymer(at)uoregon.edu are welcom
The dressed atom as binary phase modulator: towards attojoule/edge optical phase-shift keying
Nanophotonic technologies offer great promise for ultra-low power optical
signal processing, but relatively few nonlinear-optical phenomena have yet been
explored as bases for robust digital
modulation/switching~\cite{Yang07,Fara08,Liu10,Noza10}. Here we show that a
single two-level system (TLS) coupled strongly to an optical resonator can
impart binary phase modulation on a saturating probe beam. Our experiment
relies on spontaneous emission to induce occasional transitions between
positive and negative phase shifts---with each such edge corresponding to a
dissipated energy of just one photon ( aJ)---but an optical
control beam could be used to trigger additional phase switching at signalling
rates above this background. Although our ability to demonstrate controlled
switching in our atom-based experiment is limited, we discuss prospects for
exploiting analogous physics in a nanophotonic device incorporating a quantum
dot as the TLS to realize deterministic binary phase modulation with control
power in the aJ/edge regime.Comment: 7 pages, 4 figure
Control of quantum phenomena: Past, present, and future
Quantum control is concerned with active manipulation of physical and
chemical processes on the atomic and molecular scale. This work presents a
perspective of progress in the field of control over quantum phenomena, tracing
the evolution of theoretical concepts and experimental methods from early
developments to the most recent advances. The current experimental successes
would be impossible without the development of intense femtosecond laser
sources and pulse shapers. The two most critical theoretical insights were (1)
realizing that ultrafast atomic and molecular dynamics can be controlled via
manipulation of quantum interferences and (2) understanding that optimally
shaped ultrafast laser pulses are the most effective means for producing the
desired quantum interference patterns in the controlled system. Finally, these
theoretical and experimental advances were brought together by the crucial
concept of adaptive feedback control, which is a laboratory procedure employing
measurement-driven, closed-loop optimization to identify the best shapes of
femtosecond laser control pulses for steering quantum dynamics towards the
desired objective. Optimization in adaptive feedback control experiments is
guided by a learning algorithm, with stochastic methods proving to be
especially effective. Adaptive feedback control of quantum phenomena has found
numerous applications in many areas of the physical and chemical sciences, and
this paper reviews the extensive experiments. Other subjects discussed include
quantum optimal control theory, quantum control landscapes, the role of
theoretical control designs in experimental realizations, and real-time quantum
feedback control. The paper concludes with a prospective of open research
directions that are likely to attract significant attention in the future.Comment: Review article, final version (significantly updated), 76 pages,
accepted for publication in New J. Phys. (Focus issue: Quantum control
Quantum homodyne tomography with a priori constraints
I present a novel algorithm for reconstructing the Wigner function from
homodyne statistics. The proposed method, based on maximum-likelihood
estimation, is capable of compensating for detection losses in a numerically
stable way.Comment: 4 pages, REVTeX, 2 figure
Capacity of a Nonlinear Optical Channel with Finite Memory
The channel capacity of a nonlinear, dispersive fiber-optic link is
revisited. To this end, the popular Gaussian noise (GN) model is extended with
a parameter to account for the finite memory of realistic fiber channels. This
finite-memory model is harder to analyze mathematically but, in contrast to
previous models, it is valid also for nonstationary or heavy-tailed input
signals. For uncoded transmission and standard modulation formats, the new
model gives the same results as the regular GN model when the memory of the
channel is about 10 symbols or more. These results confirm previous results
that the GN model is accurate for uncoded transmission. However, when coding is
considered, the results obtained using the finite-memory model are very
different from those obtained by previous models, even when the channel memory
is large. In particular, the peaky behavior of the channel capacity, which has
been reported for numerous nonlinear channel models, appears to be an artifact
of applying models derived for independent input in a coded (i.e., dependent)
scenario
- âŠ