PAM-4 Transmission at 1550nm using Photonic Reservoir Computing Post-processing
Apostolos Argyris, Juli\'an Bueno, Ingo Fischer

TL;DR
This paper demonstrates that photonic reservoir computing effectively enhances decoding of PAM-4 signals in fiber optic transmission, enabling longer distances at high power levels without additional digital signal processing.
Contribution
It introduces the use of photonic reservoir computing as a post-processing method for high-power PAM-4 fiber transmission, achieving significant transmission distances without complex DSP.
Findings
Achieved 27 km at 56 Gb/s and 5.5 km at 112 Gb/s in simulations.
Experimental demonstration reached 21 km and 4.6 km respectively.
RC effectively handles nonlinear distortions at high power levels.
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 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 14dBm. Higher power levels…
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