PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become in...
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doaj-38064f4cebae409dbaa7cd6604e4c19d2021-03-29T22:12:02ZengIEEEIEEE Access2169-35362019-01-017370173702510.1109/ACCESS.2019.29054228669764PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-ProcessingApostolos Argyris0https://orcid.org/0000-0003-2847-7719Julian Bueno1Ingo Fischer2Campus UIB, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, SpainCampus UIB, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, SpainCampus UIB, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, SpainThe efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become insufficient or slow. Moreover, the required signal-to-noise ratio (SNR) of the received signal can be challenging, especially when adopting modulation formats with multi-level encoding. Lately, photonic reservoir computing (RC)-a hardware machine learning technique with recurrent connectivity-has been proposed as a post-processing tool that deals with deterministic distortions from fiber transmission. Here, we show that RC post-processing is remarkably efficient for multilevel encoding and for the use of very high launched optical peak power for fiber transmission up to 14 dBm. Higher power levels provide the desired high SNR values at the receiver end, at the expense of a complex nonlinear transformation of the transmission signal. Our demonstration evaluates a direct fiber communication link with 4-level pulse amplitude modulation (PAM-4) encoding and direct detection, without including optical amplification, dispersion compensation, pulse shaping or other digital signal processing (DSP) techniques. By applying RC post-processing on the distorted signal, we numerically estimate fiber transmission distances of 27 km at 56 Gb/s and of 5.5 km at 112 Gb/s data encoding rates, while fulfilling the hard-decision forward error correction (HD-FEC) bit-error-rate (BER) limit for data recovery. In an experimental equivalent demonstration of our photonic reservoir, the achieved distances are 21 and 4.6 km, respectively.https://ieeexplore.ieee.org/document/8669764/Machine learningnonlinear dynamicsoptical signal processingreservoir computingsemiconductor lasers |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Apostolos Argyris Julian Bueno Ingo Fischer |
spellingShingle |
Apostolos Argyris Julian Bueno Ingo Fischer PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing IEEE Access Machine learning nonlinear dynamics optical signal processing reservoir computing semiconductor lasers |
author_facet |
Apostolos Argyris Julian Bueno Ingo Fischer |
author_sort |
Apostolos Argyris |
title |
PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing |
title_short |
PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing |
title_full |
PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing |
title_fullStr |
PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing |
title_full_unstemmed |
PAM-4 Transmission at 1550 nm Using Photonic Reservoir Computing Post-Processing |
title_sort |
pam-4 transmission at 1550 nm using photonic reservoir computing post-processing |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become insufficient or slow. Moreover, the required signal-to-noise ratio (SNR) of the received signal can be challenging, especially when adopting modulation formats with multi-level encoding. Lately, photonic reservoir computing (RC)-a hardware machine learning technique with recurrent connectivity-has been proposed as a post-processing tool that deals with deterministic distortions from fiber transmission. Here, we show that RC post-processing is remarkably efficient for multilevel encoding and for the use of very high launched optical peak power for fiber transmission up to 14 dBm. Higher power levels provide the desired high SNR values at the receiver end, at the expense of a complex nonlinear transformation of the transmission signal. Our demonstration evaluates a direct fiber communication link with 4-level pulse amplitude modulation (PAM-4) encoding and direct detection, without including optical amplification, dispersion compensation, pulse shaping or other digital signal processing (DSP) techniques. By applying RC post-processing on the distorted signal, we numerically estimate fiber transmission distances of 27 km at 56 Gb/s and of 5.5 km at 112 Gb/s data encoding rates, while fulfilling the hard-decision forward error correction (HD-FEC) bit-error-rate (BER) limit for data recovery. In an experimental equivalent demonstration of our photonic reservoir, the achieved distances are 21 and 4.6 km, respectively. |
topic |
Machine learning nonlinear dynamics optical signal processing reservoir computing semiconductor lasers |
url |
https://ieeexplore.ieee.org/document/8669764/ |
work_keys_str_mv |
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