Zobrazeno 1 - 10
of 234
pro vyhledávání: '"Jaouën, Yves"'
We report the design and demonstration of novel 2 $\mu$m band Watt-level fiber amplifiers, fiber lasers, and wideband ASE sources that are pumped with broad spectrum Watt-level ASE sources instead of conventional fiber laser pumps. We show good agree
Externí odkaz:
http://arxiv.org/abs/2404.11200
We introduce a novel sign-dependent metric: the energy dispersion index (EDI) of sequences that endured chromatic dispersion, denoted as D-EDI, which exhibits a more accurate opposite variations with the transmission performance compared to the stand
Externí odkaz:
http://arxiv.org/abs/2312.13780
Autor:
Darweesh, Jamal, Costa, Nelson, Napoli, Antonio, Spinnler, Bernhard, Jaouen, Yves, Yousefi, Mansoor
The quantization of neural networks for the mitigation of the nonlinear and components' distortions in dual-polarization optical fiber transmission is studied. Two low-complexity neural network equalizers are applied in three 16-QAM 34.4 GBaud transm
Externí odkaz:
http://arxiv.org/abs/2310.05950
Autor:
Abu-Romoh, Mohannad, Costa, Nelson, Jaouën, Yves, Napoli, Antonio, Pedro, João, Spinnler, Bernhard, Yousefi, Mansoor
In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using
Externí odkaz:
http://arxiv.org/abs/2307.06821
Autor:
Abu-romoh, Mohannad, Costa, Nelson, Napoli, Antonio, Pedro, João, Jaouën, Yves, Yousefi, Mansoor
A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity.
Externí odkaz:
http://arxiv.org/abs/2210.05454
Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fi
Externí odkaz:
http://arxiv.org/abs/2207.12154
Autor:
Abu-romoh, Mohannad, Costa, Nelson, Napoli, Antonio, Spinnler, Bernhard, Jaouën, Yves, Yousefi, Mansoor
Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improvement of 1.8 dB and 1.2 dB, respectively, over linear equalization and a
Externí odkaz:
http://arxiv.org/abs/2205.11376
Autor:
Darweesh, Jamal, Costa, Nelson, Napoli, Antonio, Spinnler, Bernhard, Jaouen, Yves, Yousefi, Mansoor
A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive power-of-two quantization at 6 bits incurs a negligible Q-factor penal
Externí odkaz:
http://arxiv.org/abs/2205.11284
We report successful joint operation of quantum and classical communications with shared hardware. Leveraging information learned from the classical DSP, low-noise quantum communications (0.009 SNU at 15 km) compatible with 15 Mbit/s QKD is demonstra
Externí odkaz:
http://arxiv.org/abs/2202.06942
Autor:
Amavigan, Alexandre, Guyonnet, Clément, Walasik, Wiktor, Tench, Robert E., Delavaux, Jean-Marc, Robin, Thierry, Cadier, Benoit, Laurent, Arnaud, Crochet, Patrice, Jaouen, Yves
Publikováno v:
In Optical Fiber Technology May 2024 84