PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing

Apostolos Argyris, Julián Bueno, Ingo Fischer

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.
LanguageEnglish
Article number8669764
Pages37017 - 37025
Number of pages9
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 19 Mar 2019

Fingerprint

Pulse amplitude modulation
Photonics
Fibers
Processing
Signal to noise ratio
Signal processing
Demonstrations
Pulse shaping
Dispersion compensation
Forward error correction
Digital signal processing
Bit error rate
Telecommunication links
Amplification
Decoding
Learning systems
Modulation
Hardware
Recovery

Keywords

  • information processing
  • neural network
  • neural network application
  • semiconductor diode-laser
  • delay effects
  • nonlinear dynamics

Cite this

Argyris, Apostolos ; Bueno, Julián ; Fischer, Ingo. / PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing. In: IEEE Access. 2019 ; Vol. 7. pp. 37017 - 37025.
@article{467e1a72dc974d1e869f9b4b52237877,
title = "PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing",
abstract = "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.",
keywords = "information processing, neural network, neural network application, semiconductor diode-laser, delay effects, nonlinear dynamics",
author = "Apostolos Argyris and Juli{\'a}n Bueno and Ingo Fischer",
year = "2019",
month = "3",
day = "19",
doi = "10.1109/ACCESS.2019.2905422",
language = "English",
volume = "7",
pages = "37017 -- 37025",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE",

}

PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing. / Argyris, Apostolos; Bueno, Julián; Fischer, Ingo.

In: IEEE Access, Vol. 7, 8669764, 19.03.2019, p. 37017 - 37025.

Research output: Contribution to journalArticle

TY - JOUR

T1 - PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing

AU - Argyris, Apostolos

AU - Bueno, Julián

AU - Fischer, Ingo

PY - 2019/3/19

Y1 - 2019/3/19

N2 - 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.

AB - 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.

KW - information processing

KW - neural network

KW - neural network application

KW - semiconductor diode-laser

KW - delay effects

KW - nonlinear dynamics

U2 - 10.1109/ACCESS.2019.2905422

DO - 10.1109/ACCESS.2019.2905422

M3 - Article

VL - 7

SP - 37017

EP - 37025

JO - IEEE Access

T2 - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8669764

ER -