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 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 harddecision 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. he joined the Institute for Cross-Disciplinary Physics and Complex Systems -IFISC (CSIC-UIB) in Spain as a Marie-Skłodowska Curie Fellow. He has participated in 12 research projects, funded by the European Union and the national Greek / Spanish research authorities. He has published more than 80 papers in peer-reviewed journals and conference proceedings, while he is the main author of 3 book chapters. His research interests include nonlinear dynamics in semiconductor lasers, photonic devices and coupled systems, optical communication systems, chaotic encryption, optical sensing and machine learning techniques.

Comprehensive Performance Analysis of a VCSEL-Based Photonic Reservoir Computer

“…RC employs a reservoir where the nodes are randomly interconnected along with self-connections. This creates intrinsic memory inside the ANN, an especially helpful feature in complex data prediction tasks [5], [6]. Furthermore, RC systems can be built with incredibly simplified photonic hardware using a single nonlinear element and a delay line [7].…”

Reservoir-Computing Based Equalization With Optical Pre-Processing for Short-Reach Optical Transmission

“…Losing the information carried by the field's phase makes chromatic dispersion (CD) the major obstacle to extending the transmission reach. Several techniques are available for compensating for the intersymbol interference (ISI) induced by CD, by acting in the optical domain [2], [3], in the digital/electric domain [4], [5], [6], [7], [8], [9], [10], [11], [12] or by considering a joint optoelectronic approach [13], [14], [15], [16], [17], [18], [20]. Optical dispersion compensation techniques mainly rely on negative dispersion media, such as dispersion-compensating fibers (DCFs) or fiber Bragg gratings (FBGs).…”

“…This may be achieved by moving to hybrid optoelectronic approaches, which leverage on sharing the complexity between optical and electrical domain, at the expense of only small added optical power loss, as we have shown in [20]. Additionally, a few recent investigations have also looked into applying a new machine learning paradigm known as reservoir computing (RC), which allows avoiding to train the hidden part (reservoir) of the neural network [13], [14], [15], [17], [18], [19]. By providing an optoelectronic implementation of the system, significant gains in transmission reach have been demonstrated.…”

Comparison of Photonic Reservoir Computing Systems for Fiber Transmission Equalization

“…In particular, RC based on photonic components have been successfully used to solve various classification tasks with high speed and predicting complex timeseries with an extended prediction horizon. Moreover, photonic RC implementations have been recently demonstrated as alternative to digital signal processing solutions for signal recovery in optical transmission systems [6]- [8]. Photonic RC implementations that have been introduced so far are based on delay-coupled semiconductor lasers and semiconductor optical amplifiers [6], [9]- [12], optoelectronic delay systems [13]- [17], passive photonic elements [18], photonic crystal cavities [19] and silicon photonic chips [20], [21], or fiber nonlinearities [7], [22] as the photonic nonlinear elements for computation, see for a review [23].…”