MMF-based Data Center Interconnect using Commercial Coherent Transceivers

In this manuscript we present an experimental analysis of a multi-mode fiber (MMF) link based on coherent detection. We propose the use of a commercial coherent transceiver to increase the transmission speed of a short-reach optical communication system over 300 m OM3 fiber. The performance of the system is studied in terms of bit rate as a function of the power budget margin (PBM) defined as the extra attenuation that can be introduced on the optical path. We investigate polarization multiplexed (PM) QPSK and 16-QAM modulation formats achieving up to 400 Gbps net bit rate transmission. Moreover, we analyze the impact of the lateral offset introduced by the connectors between MMF segments, showing decreasing PBM for increasing lateral offset. Nevertheless, PBM in excess of 28 dB for PM-QPSK modulation at 100G and 200G, 23 dB for 200G PM-16QAM modulation and 16 dB for 400G PM-16QAM, shows that commercial coherent transceivers can be used on MMF links up to much higher bit rates than those achieved by current VCSEL+direct detection based systems, provided that connections along the MMF have connectors connectors with offsets in the 3 μm to 6 μm range

Experimental VCSEL Digital Twin modeling for net 100 Gb/s/λ nonlinear Digital Pre-Distortion

We experimentally model a VCSEL-based optical transmitter for high speed intra data center interconnects using a convolutional neural network digital twin. The device is able to effectively reproduce the VCSEL linear and nonlinear distortions on PAM4 signals transmitted at 107.2 Gbps, thus enabling the optimization of nonlinear VCSEL-MMF digital pre-distorters

End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

We propose a novel optimization method for a Neural Network based Digital Pre-Distorter (DPD), applied in Intensity Modulation-Direct Detection transmission systems leveraging Multi-Modal Fiber and Vertical-Cavity Surface-Emitting Laser. We train the DPD using End-to-end Deep Learning of the optical link, together with a Direct Learning Approach leveraging experimental measurements for modeling the transmission channel. The optimization considers VCSEL amplitude constraints, the use of an FFE at the receiver side, and the presence of a receiver non-flat Colored Gaussian Noise (CGN). We verify our optimized DPD on an experimental setup transmitting a 92 Gbps PAM-4 modulated signal. We achieve, for BER=0.01, a performance gain of more than 1 dB in terms of Optical Path Loss with respect to the best performing non-pre-distorted scenario

Nonlinear Pre-distortion through a Multi-rate End-to-end Learning Approach over VCSEL-MMF IM-DD Optical Links

We experimentally demonstrate a nonlinear digital pre-distorter for PAM-M shaping in VCSEL+MMF IM-DD links able to operate at a generic baud rate using a fractional sample-per-symbol Neural Network. We focus on efficient and practical multi-rate operation, signal amplitude constraints, and linear equalizer at the receiver

An Analytical Model for Performance Estimation in High-Capacity IMDD Systems

In this paper, we propose an analytical model to estimate the signal-to-noise ratio (SNR) at the output of an adaptive equalizer in intensity modulation and direct detection (IMDD) optical transmission systems affected by shot noise, thermal noise, relative intensity noise (RIN), chromatic dispersion (CD) and bandwidth limitations. We develop the model as an extension of a previously presented one, and then we test its accuracy by sweeping the main parameters of a 4-PAM-based communication system such as RIN coefficient, extinction ratio, CD coefficient and equalizer memory. Our findings show a remarkable agreement between time-domain simulations and analytical results, with SNR discrepancies below 0.1 dB in most cases, for both feed-forward and decision-feedback equalization. We consider that the proposed model is a powerful tool for the numerical design of strongly band-limited IMDD systems using receiver equalization, as it happens in most of modern and future M-PAM solutions for short reach and access systems

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

EGN model of non-linear fiber propagation

The GN-model has been proposed as an approximate but sufficiently accurate tool for predicting uncompensated optical coherent transmission system performance, in realistic scenarios. For this specific use, the GN-model has enjoyed substantial validation, both simulative and experimental. Recently, however, it has been pointed out that its predictions, when used to obtain a detailed picture of non-linear interference (NLI) noise accumulation along a link, may be affected by a substantial NLI overestimation error, especially in the first spans of the link. In this paper we analyze in detail the GN-model errors. We discuss recently proposed formulas for correcting such errors and show that they neglect several contributions to NLI, so that they may substantially underestimate NLI in specific situations, especially over low-dispersion fibers. We derive a complete set of formulas accounting for all single, cross, and multi-channel effects, This set constitutes what we have called the enhanced GN-model (EGN-model). We extensively validate the EGN model by comparison with accurate simulations in several different system scenarios. The overall EGN model accuracy is found to be very good when assessing detailed span-by-span NLI accumulation and excellent when estimating realistic system maximum reach. The computational complexity vs. accuracy trade-offs of the various versions of the GN and EGN models are extensively discussed

Design rules for reach maximization in uncompensated Nyquist-WDM links

We propose analytical design rules to predict relative maximum reach variations in NyWDM uncompensated links. Tradeoffs among system parameters are shown. Validation is demonstrated using experimental data. The method can be used also for comparison of different modulation format

Adaptive equalization in coherent receivers using a Stokes space update algorithm

A coherent optical receiver including an optical transducer, an adaptive filter, and a processor updates the adaptive filter according to a metric derived in Stokes space. The optical transducer receives an optical signal corresponding to a modulated signal of symbols. The optical transducer also determines a first signal corresponding to a first polarization of the optical signal and a second signal corresponding to a second polarization of the optical signal. The adaptive filter recovers a first equalized signal and a second equalized signal from the first signal and the second signal. The first equalized signal and the second equalized signal form an equalized modulated signal of symbols. The processor calculates a set of Stokes parameters from the equalized modulated signal and updates the adaptive filter based on a metric derived from the set of Stokes parameters