Flexible and Autonomous Multi-band Raman Amplifiers

We propose an embedded controller able to autonomously manage Raman amplification in software-defined optical networks. The conceived structure allows the system to work both in single and multi-band transmission, achieving a large range of amplification constraints. A set of experiments validates this proposal

Comparison of DSP-based TDMA and FDMA channel aggregation techniques in mobile fronthauling

Abstract Cloud Radio Access Network (C-RAN) is perceived as a future essential technology to satisfy the ever-increasing demand of mobile data traffic. Considerable research efforts are expending in the optimization of C-RAN architecture. In this paper, we perform a comparison of two DSP-based fronthauling techniques for aggregation of radio waveforms: time division multiple access (TDMA) and frequency division multiple access (FDMA), in terms of error vector magnitude (EVM), spectral bandwidth efficiency and digital signal processing (DSP) complexity as a performance metrics. The two techniques are compared by means of simulation and validated experimentally on an intensity modulation and direct detection (IM-DD) optical fronthaul link capable of aggregating 48 and 96 LTE-A (20 MHz) channels. Moreover, we made simulation comparison on 24 (100 MHz) new radio (NR) waveforms which will be used in the upcoming 5G applications. We reveal that there is ∼50% and ∼20% spectral efficiency gain by TDMA aggregation on LTE-A and NR waveforms respectively. Hence TDMA gives slightly better performance in the case of 96 LTE-A channels which is attributed to slightly better linearity over the optical channel frequency response for larger number of channel. In addition, we show that TDMA is more efficient in terms of complexity than FDMA system that requires an additional pre-emphasis technique to equalize the overall per channel performance

A Multi-Rate Approach for Nonlinear Pre-Distortion Using End-to-End Deep Learning in IM-DD Systems

Modern intra-data center (IDC) interconnects leverage robust and low-cost intensity modulation (IM) and direct detection (DD) optical links, based on multimode fibers (MMFs) and vertical-cavity surface-emitting lasers (VCSELs). Current solutions, based on on-off keying (OOK) modulations, reach up to 25-50 Gbps per lane over nearly 100 meters. The actual target for IDCs is to increase VCSEL-MMF links capacity up to 100 Gbps, using PAM-4 on the same devices. To counteract the consequent linear and nonlinear distortions affecting the transmitted signals, an effective solution is to exploit digital signal processing (DSP). In this manuscript, we propose a novel method to optimize a nonlinear artificial neural network (ANN) digital pre-distorter (DPD), based on End-to-end (E2E) learning, that, trained jointly with a Feed-Forward Equalizer (FFE), fulfills physical amplitude constraints and handles different ratio between the sampling rates incurring along with an optical IM-DD system. We indeed propose an E2E ANN system operating simultaneously at different sampling frequencies. We moreover propose in our training method a substitution to the time-domain injection of the receiver noise in the system with an additive regularization term in the FFE gradient loss. We experimentally show the advantages of our proposed DPD comparing the bit error rate (BER) performance against the same scenario without DPD. We assess the gain in terms of Gross Bit Rate and Optical Path Loss (OPL), at given BER targets, for different fiber lengths

Non-Linear SNR Degradation of Mixed 10G/100G Transmission Over Dispersion-Managed Networks

Enabling the mixed 10G IMDD with 100G coherent channels transmission over legacy dispersion-managed links on metro network chunks will come in handy for the operators to increase network flexibility while saving on CAPEX and operate progressive upgrades with no impact on existing traffic. We developed a semi-analytical model for 10G-to-100G XPM noise allowing QoT estimation on mixed 10G/100G systems

Local vs. Global Optimization for Optical Line System Control in Disaggregated Networks

Setting the operating point of optical amplifiers of optical line systems (OLS)s within transparent, disaggregated and reconfigurable networks is a crucial task that determines the optical transmission performance of the specific infrastructure. In this work, four optimization strategies for OLS control are compared through a simulation campaign, where a realistic physical layer is replicated using a machine-learning model derived from an experimental dataset on commercial devices for the Erbium-doped fiber amplifiers (EDFA)s and a characterized set of fiber spans. In particular, two distinct objective functions are evaluated, both at the end of the line (global approach), and, in turn, at the end of each single span (local approach)

Autonomous Physical Layer Characterization in Cognitive Optical Line Systems

We develop a procedure to autonomously characterize the optical line system physical layer, span-by-span, using in-line OTDRs and OCMs. This procedure has been experimentally validated, showing a clear correlation between the experimental outcomes and emulations