801 research outputs found
Experimental Demonstration of Dual Polarization Nonlinear Frequency Division Multiplexed Optical Transmission System
Multi-eigenvalues transmission with information encoded simultaneously in
both orthogonal polarizations is experimentally demonstrated. Performance below
the HD-FEC limit is demonstrated for 8-bits/symbol 1-GBd signals after
transmission up to 207 km of SSMF
Dual polarization nonlinear Fourier transform-based optical communication system
New services and applications are causing an exponential increase in internet
traffic. In a few years, current fiber optic communication system
infrastructure will not be able to meet this demand because fiber nonlinearity
dramatically limits the information transmission rate. Eigenvalue communication
could potentially overcome these limitations. It relies on a mathematical
technique called "nonlinear Fourier transform (NFT)" to exploit the "hidden"
linearity of the nonlinear Schr\"odinger equation as the master model for
signal propagation in an optical fiber. We present here the theoretical tools
describing the NFT for the Manakov system and report on experimental
transmission results for dual polarization in fiber optic eigenvalue
communications. A transmission of up to 373.5 km with bit error rate less than
the hard-decision forward error correction threshold has been achieved. Our
results demonstrate that dual-polarization NFT can work in practice and enable
an increased spectral efficiency in NFT-based communication systems, which are
currently based on single polarization channels
Experimental Verification of Rate Flexibility and Probabilistic Shaping by 4D Signaling
The rate flexibility and probabilistic shaping gain of -dimensional
signaling is experimentally tested for short-reach, unrepeated transmission. A
rate granularity of 0.5 bits/QAM symbol is achieved with a distribution matcher
based on a simple look-up table.Comment: Presented at OFC'18, San Diego, CA, US
Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers
We experimentally validate a real-time machine learning framework, capable of
controlling the pump power values of Raman amplifiers to shape the signal power
evolution in two-dimensions (2D): frequency and fiber distance. In our setup,
power values of four first-order counter-propagating pumps are optimized to
achieve the desired 2D power profile. The pump power optimization framework
includes a convolutional neural network (CNN) followed by differential
evolution (DE) technique, applied online to the amplifier setup to
automatically achieve the target 2D power profiles. The results on achievable
2D profiles show that the framework is able to guarantee very low maximum
absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles.
Moreover, the framework is tested in a multi-objective design scenario where
the goal is to achieve the 2D profiles with flat gain levels at the end of the
span, jointly with minimum spectral excursion over the entire fiber length. In
this case, the experimental results assert that for 2D profiles with the target
flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when
the setup is not physically limited in the pump power values. The simulation
results also prove that with enough pump power available, better gain deviation
(less than 0.6 dB) for higher target gain levels is achievable
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
We report a neural-network based erbium-doped fiber amplifier (EDFA) gain
model built from experimental measurements. The model shows low gain-prediction
error for both the same device used for training (MSE 0.04 dB) and
different physical units of the same make (generalization MSE 0.06
dB)
Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
End-to-end learning has become a popular method to optimize a constellation
shape of a communication system. When the channel model is differentiable,
end-to-end learning can be applied with conventional backpropagation algorithm
for optimization of the shape. A variety of optimization algorithms have also
been developed for end-to-end learning over a non-differentiable channel model.
In this paper, we compare gradient-free optimization method based on the
cubature Kalman filter, model-free optimization and backpropagation for
end-to-end learning on a fiber-optic channel modeled by the split-step Fourier
method. The results indicate that the gradient-free optimization algorithms
provide a decent replacement to backpropagation in terms of performance at the
expense of computational complexity. Furthermore, the quantization problem of
finite bit resolution of the digital-to-analog and analog-to-digital converters
is addressed and its impact on geometrically shaped constellations is analysed.
Here, the results show that when optimizing a constellation with respect to
mutual information, a minimum number of quantization levels is required to
achieve shaping gain. For generalized mutual information, the gain is
maintained throughout all of the considered quantization levels. Also, the
results implied that the autoencoder can adapt the constellation size to the
given channel conditions
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