56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes

TL;DR

This paper demonstrates two photonic post-processing schemes for high-speed fiber transmission, showing that photonic reservoir computing's effectiveness depends on signal properties and that simpler extreme learning machines can match reservoir performance with faster computation.

Contribution

The work introduces experimental photonic post-processing schemes for 56 GBaud PAM-4 fiber transmission and compares reservoir computing with extreme learning machines, highlighting system simplification and performance.

Findings

Photonic reservoir memory's limited role with chromatic dispersion.

Reservoir computing and extreme learning machines achieve similar data recovery.

Photonic equalization outperforms DSP-based receivers at high OSNR.

Abstract

Analog photonic computing has been proposed and tested in recent years as an alternative approach for data recovery in fiber transmission systems. Photonic reservoir computing, performing nonlinear transformations of the transmitted signals and exhibiting internal fading memory, has been found advantageous for this kind of processing. In this work, we show that the effectiveness of the internal fading memory depends significantly on the properties of the signal to be processed. Specifically, we demonstrate two experimental photonic post-processing schemes for a 56 GBaud PAM-4 experimental transmission system, with 100 km uncompensated standard single-mode fiber and direct detection. We show that, for transmission systems with significant chromatic dispersion, the contribution of a photonic reservoir's fading memory to the computational performance is limited. In a comparison between the data recovery performances between a reservoir computing and an extreme learning machine fiber-based configuration, we find that both offer equivalent data recovery. The extreme learning machine approach eliminates the necessity of external recurrent connectivity, which simplifies the system and increases the computation speed. Above 31 dB OSNR, the photonics-based equalization exhibits a lower BER than the respective offline DSP-based KK receiver.