OFC 2019: massive participation by PhotoNext members!

Several members of the PhotoNext team will participate next week to OFC2019, the most important conference worldwide on optical communication and networking. https://www.ofcconference.org/en-us/home/ 

Besides an important list of presentations, we are proud to mention that one of the three General Chairs of the conference is Gabriella Bosco. Congratulations to Gabriella!

Here is the list of papers that Photonext members will present at the conference

• Sunday Workhop: “Will Advanced Direct-detection Systems Ever Be the Solution of Choice for Metro and Access Applications?” Roberto Gaudino presentation on Challenges of Coherent in Passive Optical Networks
• W4J.1 • 16:30 Burst-mode Equalization Strategies in 25 Gbps US-PON Using Duobinary and 10G-class APD for 20-km in C-band, Pablo Torres-Ferrera1 , Valerio Milite1 , Valter Ferrero1 , Maurizio Valvo2 , Roberto Mercinelli2 , Roberto Gaudino1 ; 1 Politecnico di Torino, Italy; 2 Telecom Italia (TIM), Italy. 25 Gbps burst-mode upstream duobinary transmission in C-band using 10G optoelectronics and APD-based adaptive equalization receiver is analyzed and experimentally demonstrated. We show a memory-aided alternative that avoids the long training preambles (>2600 bits) needed in commonly proposed memoryless approaches.
• M1I.2 • 08:15 Low-Complexity Non-linear Phase Noise Mitigation Using a Modified Soft-decoding Strategy, Dario Pilori1, Antonino Nespola2, Pierluigi Poggiolini1 , Fabrizio Forghieri3 , Gabriella Bosco1 ; 1 DET, Politecnico di Torino, Italy; 2 LINKS Foundation, Italy; 3 Cisco Photonics Italy srl, Italy.We propose a modified soft-decoding strategy that takes into account residual nonlinear phase noise. We show the effectiveness of this method in a multi-span experiment with propagation over legacy fibers using uniform and probabilistic-shaped constellations.  
• M1I.4 • 08:45 Accurate Non-linearity Fully-closed-form Formula Based on the GN/EGN Model and Large-data-set Fitting, Pierluigi Poggiolini1, Mahdi Ranjbar Zefreh1, Gabriella Bosco1 , Fabrizio Forghieri2 , Stefano Piciaccia2 ; 1 Politecnico di Torino, Italy; 2 Cisco Photonics Italy srl, Italy. We tested the accuracy of a fully-closed-form GN/EGN formula over 1,700 different fully-loaded systems. We improved it greatly through a correction that leverages the large data-set, providing an effective tool for real-time physical-layer-aware network management.
• M1J.1 • 08:00 Machine Learning-based Raman Amplifier Design, Darko Zibar1 , Alessio Ferrari2 , Vittorio Curri2 , Andrea Carena2 ; 1 Danmarks Teknishe Universitet, Denmark; 2 Politecnico di Torino, Italy. A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.