Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Prilepsky, Jaroslaw"'
This paper introduces a new machine learning-assisted chromatic dispersion compensation filter, demonstrating its superior power efficiency compared to conventional FFT-based filters for metro link distances. Validations on FPGA confirmed an energy e
Externí odkaz:
http://arxiv.org/abs/2409.13381
Power efficiency remains a significant challenge in modern optical fiber communication systems, driving efforts to reduce the computational complexity of digital signal processing, particularly in chromatic dispersion compensation (CDC) algorithms. W
Externí odkaz:
http://arxiv.org/abs/2409.10416
Autor:
Duque, Alice, Freire, Pedro, Manuylovich, Egor, Stoliarov, Dmitrii, Prilepsky, Jaroslaw, Turitsyn, Sergei
This work tackles the critical challenge of mitigating "hardware noise" in deep analog neural networks, a major obstacle in advancing analog signal processing devices. We propose a comprehensive, hardware-agnostic solution to address both correlated
Externí odkaz:
http://arxiv.org/abs/2409.08633
Publikováno v:
Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky, and Sergei K. Turitsyn, "Artificial neural networks for photonic applications - from algorithms to implementation: tutorial," Adv. Opt. Photon. 15, 739-834 (2023)
This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at th
Externí odkaz:
http://arxiv.org/abs/2408.02685
Publikováno v:
Proc. R. Soc. A 480 (2024), 20230828
We consider the Riemann--Hilbert (RH) approach to the construction of periodic finite-band solutions to the focusing nonlinear Schr\"odinger (NLS) equation. An RH problem for the solution of the finite-band problem has been recently derived via the F
Externí odkaz:
http://arxiv.org/abs/2311.16902
Autor:
Srivallapanondh, Sasipim, Freire, Pedro J., Alam, Ashraful, Costa, Nelson, Spinnler, Bernhard, Napoli, Antonio, Sedov, Egor, Turitsyn, Sergei K., Prilepsky, Jaroslaw E.
For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in lau
Externí odkaz:
http://arxiv.org/abs/2307.05374
Autor:
Srivallapanondh, Sasipim, Freire, Pedro J., Napoli, Antonio, Turitsyn, Sergei K., Prilepsky, Jaroslaw E.
To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.
Externí odkaz:
http://arxiv.org/abs/2305.09495
Autor:
Freire, Pedro J., Srivallapanondh, Sasipim, Anderson, Michael, Spinnler, Bernhard, Bex, Thomas, Eriksson, Tobias A., Napoli, Antonio, Schairer, Wolfgang, Costa, Nelson, Blott, Michaela, Turitsyn, Sergei K., Prilepsky, Jaroslaw E.
In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing
Externí odkaz:
http://arxiv.org/abs/2212.04703
Autor:
Srivallapanondh, Sasipim, Freire, Pedro J., Spinnler, Bernhard, Costa, Nelson, Napoli, Antonio, Turitsyn, Sergei K., Prilepsky, Jaroslaw E.
To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38\% latency decrease, while impacting the Q-facto
Externí odkaz:
http://arxiv.org/abs/2212.04569
Autor:
Freire, Pedro J., Napoli, Antonio, Ron, Diego Arguello, Spinnler, Bernhard, Anderson, Michael, Schairer, Wolfgang, Bex, Thomas, Costa, Nelson, Turitsyn, Sergei K., Prilepsky, Jaroslaw E.
In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in high-speed coherent optical transmission systems. In this work, we provide a comp
Externí odkaz:
http://arxiv.org/abs/2208.12866