PAM-4 Transmission at 1550nm 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 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 provide the desired high signal-to-noise ratio (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 27km at 56Gb/s and of 5.5 km at 112Gb/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 21km and 4.6km respectively.

References in corpus (4)

• Optoelectronic Reservoir Computing
• High performance photonic reservoir computer based on a coherently driven passive cavity
• Applying Neural Networks in Optical Communication Systems: Possible Pitfalls
• Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation

Cited by in corpus (6)

• Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
• High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks
• Reservoir Computing with Noise
• Time Shifts to Reduce the Size of Reservoir Computers
• Creating New Chaotic Signals with Reservoir Computers
• 56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes