Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing.

Microring resonators (MRRs) are promising devices for time-delay photonic reservoir computing, but the impact of the different physical effects taking place in the MRRs on the reservoir computing performance is yet to be fully understood. We numerically analyze the impact of linear losses as well as thermo-optic and free-carrier effects relaxation times on the prediction error of the time-series task NARMA-10. We demonstrate the existence of three regions, defined by the input power and the frequency detuning between the optical source and the microring resonance, that reveal the cavity transition from linear to nonlinear regimes. One of these regions offers very low error in time-series prediction under relatively low input power and number of nodes while the other regions either lack nonlinearity or become unstable. This study provides insight into the design of the MRR and the optimization of its physical properties for improving the prediction performance of time-delay reservoir computing.

Addressing data scarcity in optical matrix multiplier modeling using transfer learning.

We present and experimentally evaluate the use of transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pretraining the model using synthetic data generated from a less accurate analytical model and fine-tuning it with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve <1 dB root-mean-square error on the 3×3 matrix weights implemented by a photonic chip while using only 25% of the available data.

Subspace tracking for phase noise source separation in frequency combs.

It is widely acknowledged that the phase noise of an optical frequency comb primarily stems from the common mode (carrier-envelope) and the repetition rate phase noise. However, owing to technical noise sources or other intricate intra-cavity factors, residual phase noise components, distinct from the common mode and the repetition rate phase noise, may also exist. We introduce a measurement technique that combines subspace tracking and multi-heterodyne coherent detection for the separation of different phase noise sources. This method allows us to break down the overall phase noise sources associated with a specific comb-line into distinct phase noise components associated with the common mode, the repetition rate and the residual phase noise terms. The measurement method allow us, for the first time, to identify and measure residual phase noise sources of a frequency modulated mode-locked laser.

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers.

We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup, power values of four first-order counter-propagating pumps are optimized to achieve the desired 2D power profile. The pump power optimization framework includes a convolutional neural network (CNN) followed by differential evolution (DE) technique, applied online to the amplifier setup to automatically achieve the target 2D power profiles. The results on achievable 2D profiles show that the framework is able to guarantee very low maximum absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles. Moreover, the framework is tested in a multi-objective design scenario where the goal is to achieve the 2D profiles with flat gain levels at the end of the span, jointly with minimum spectral excursion over the entire fiber length. In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values. The simulation results also prove that with enough pump power available, better gain deviation (less than 0.6 dB) for higher target gain levels is achievable.

Optimization of frequency combs spectral-flatness using evolutionary algorithm.

We demonstrate the use of meta-heuristics algorithms for flatness optimization of optical frequency combs (OFCs). Without any additional component for flatness compensation, the laser alone is explored when driven by optimized bias current and radio frequency (RF) driving signals composed by multiple harmonics. The bias current amplitude and RF harmonic amplitudes and relative phases are optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms. The numerical results lead to a 9 lines-GS-laser-based OFC spectrum with 2.9 dB flatness. An online experimental optimization using the DE algorithm results in a 7-line-GS-laser-based OFC with 2 dB flatness.

Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks.

We present a convolutional neural network architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution in both distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54, and 0.64 dB) and standard deviation set (0.62, 0.43, and 0.38 dB) of the maximum test error are obtained numerically employing two and three counter-, and four bidirectional propagating pumps, respectively.

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning.

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼0.3dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification.

Bayesian filtering framework for noise characterization of frequency combs.

Amplitude and phase noise correlation matrices are of fundamental importance for studying noise properties of frequency combs. They include information about the origin of noise sources as well as the scaling and correlation of the noise across the comb lines. These matrices provide an insight that is essential for obtaining low-noise performance which is important for, e.g., applications in optical communication, low-noise microwave signal generation, and distance measurements. Estimation of amplitude and phase noise correlation matrices requires highly-accurate measurement technique which can distinguishes between noise sources coming from the frequency comb and the measurement system itself. Bayesian filtering provides a theoretically optimum approach for filtering of measurement noise and thereby, the most accurate measurement of phase and amplitude noise. In this paper, a novel Bayesian filtering based framework for joint estimation of amplitude and phase noise of multiple frequency comb lines is proposed, and demonstrated for phase noise characterization. Compared to the conventional approaches, that do not employ any measurement noise filtering, the proposed approach provides significantly more accurate measurements of correlation matrices, operates over a wide range of signal-to-noise-ratios and gives an insight into comb's dynamics at short scales (<10-8 s).

Low-complexity carrier phase recovery based on principal component analysis for square-QAM modulation formats.

We propose, numerically analyze and experimentally demonstrate a low-complexity, modulation-order independent, non-data-aided (NDA), feed-forward carrier phase recovery (CPR) algorithm. The proposed algorithm enables synchronous decoding of arbitrary square-quadrature amplitude modulation (QAM) constellations and it is suitable for a realistic hardware implementation based on block-wise parallel processing. The proposed method is based on principal component analysis (PCA) and it outperforms the well-known and widely used blind phase search (BPS) algorithm at low signal-to-noise ratio (SNR) values, showing much lower cycle slip rate (CSR) both numerically and experimentally. For operation at higher SNR values, a hybrid two-stage implementation combining the proposed method and BPS is also proposed and their performance are investigated benchmarking them against the two-stage BPS (2S-BPS). The complexity of the proposed simple and hybrid methods are evaluated against 2S-BPS and computational complexity savings of 92% and 40% are expected for the simple and hybrid methods, respectively.

Model-Based Position and Reflectivity Estimation of Fiber Bragg Grating Sensor Arrays.

We propose an efficient model-based signal processing approach for optical fiber sensing with fiber Bragg grating (FBG) arrays. A position estimation based on an estimation of distribution algorithm (EDA) and a reflectivity estimation method using a parametric transfer matrix model (TMM) are outlined in detail. The estimation algorithms are evaluated with Monte Carlo simulations and measurement data from an incoherent optical frequency domain reflectometer (iOFDR). The model-based approach outperforms conventional Fourier transform processing, especially near the spatial resolution limit, saving electrical bandwidth and measurement time. The models provide great flexibility and can be easily expanded in complexity to meet different topologies and to include prior knowledge of the sensors. Systematic errors due to crosstalk between gratings caused by multiple reflections and spectral shadowing could be further considered with the TMM to improve the performance of large-scale FBG array sensor systems.

Compact silicon multimode waveguide spectrometer with enhanced bandwidth.

Compact, broadband, and high-resolution spectrometers are appealing for sensing applications, but difficult to fabricate. Here we show using calibration data a spectrometer based on a multimode waveguide with 2 GHz resolution, 250 GHz bandwidth, and a 1.6 mm × 2.1 mm footprint. Typically, such spectrometers have a bandwidth limited by the number of modes supported by the waveguide. In this case, an on-chip mode-exciting element is used to repeatably excite distinct collections of waveguide modes. This increases the number of independent spectral channels from the number of modes to this number squared, resulting in an extension of the usable range.

Ultrahigh-speed Si-integrated on-chip laser with tailored dynamic characteristics.

For on-chip interconnects, an ideal light source should have an ultralow energy consumption per bandwidth (operating en-ergy) as well as sufficient output power for error-free detection. Nanocavity lasers have been considered the most ideal for smaller operating energy. However, they have a challenge in obtaining a sufficient output power. Here, as an alternative, we propose an ultrahigh-speed microcavity laser structure, based on a vertical cavity with a high-contrast grating (HCG) mirror for transverse magnetic (TM) polarisation. By using the TM HCG, a very small mode volume and an un-pumped compact optical feedback structure can be realised, which together tailor the frequency response function for achieving a very high speed at low injection currents. Furthermore, light can be emitted laterally into a Si waveguide. From an 1.54-µm optically-pumped laser, a 3-dB frequency of 27 GHz was obtained at a pumping level corresponding to sub-mA. Using measured 3-dB frequen-cies and calculated equivalent currents, the modulation current efficiency factor (MCEF) is estimated to be 42.1 GHz/mA1/2, which is superior among microcavity lasers. This shows a high potential for a very high speed at low injection currents or avery small heat generation at high bitrates, which are highly desirable for both on-chip and off-chip applications.

Markov chain Monte Carlo methods for statistical analysis of RF photonic devices.

The microwave reflection coefficient is commonly used to characterize the impedance of high-speed optoelectronic devices. Error and uncertainty in equivalent circuit parameters measured using this data are systematically evaluated. The commonly used nonlinear least-squares method for estimating uncertainty is shown to give unsatisfactory and incorrect results due to the nonlinear relationship between the circuit parameters and the measured data. Markov chain Monte Carlo methods are shown to provide superior results, both for individual devices and for assessing within-die variation.

Impairment mitigation in superchannels with digital backpropagation and MLSD.

We assess numerically the performance of single-carrier digital backpropagation (SC-DBP) and maximum-likelihood sequence detection (MLSD) for DP-QPSK and DP-16QAM superchannel transmission over dispersion uncompensated links for three different cases of spectral shaping: optical pre-filtering of RZ and NRZ spectra, and digital Nyquist filtering. We investigate the limits for carrier proximity of each spectral shaping technique and the correspondent performance behavior of each algorithm, for both modulation formats. For superchannels with carrier spacing close to the Nyquist limit, it is shown that the maximum performance improvement of 1.0 dB in Q(2)-factor is provided by those algorithms. However, such gain can be highly reduced when the order of the modulation format increases.

Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission.

In this paper, we show numerically and experimentally that expectation maximization (EM) algorithm is a powerful tool in combating system impairments such as fibre nonlinearities, inphase and quadrature (I/Q) modulator imperfections and laser linewidth. The EM algorithm is an iterative algorithm that can be used to compensate for the impairments which have an imprint on a signal constellation, i.e. rotation and distortion of the constellation points. The EM is especially effective for combating non-linear phase noise (NLPN). It is because NLPN severely distorts the signal constellation and this can be tracked by the EM. The gain in the nonlinear system tolerance for the system under consideration is shown to be dependent on the transmission scenario. We show experimentally that for a dispersion managed polarization multiplexed 16-QAM system at 14 Gbaud a gain in the nonlinear system tolerance of up to 3 dB can be obtained. For, a dispersion unmanaged system this gain reduces to 0.5 dB.

Experimental demonstration of a cognitive quality of transmission estimator for optical communication systems.

The impact of physical layer impairments in optical network design and operation has received significant attention in the last years, thereby requiring estimation techniques to predict the quality of transmission (QoT) of optical connections before being established. In this paper, we report on the experimental demonstration of a case-based reasoning (CBR) technique to predict whether optical channels fulfill QoT requirements, thus supporting impairment-aware networking. The validation of the cognitive QoT estimator is performed in a WDM 80 Gb/s PDM-QPSK testbed, and we demonstrate that even with a very small and not optimized underlying knowledge base, it achieves between 79% and 98.7% successful classifications based on the error vector magnitude (EVM) parameter, and approximately 100% when the classification is based on the optical signal to noise ratio (OSNR).

High phase noise tolerant pilot-tone-aided DP-QPSK optical communication systems.

In this paper we experimentally demonstrate a novel, high phase-noise tolerant, optical dual polarization (DP) quadrature phase-shift keying (QPSK) communication system based on pilot-tone-aided phase noise cancellation (PNC) algorithm. Vertical cavity surface emitting lasers (VCSELs) with approximate 300 MHz linewidth are used as transmitters and local oscillators for coherent detection of optical DP-QPSK signals. The proposed system, with central wavelength at 1540.68 nm, operates at 40 Gb/s over 80 km single mode fiber (SMF) as part of a passive optical network (PON). The deployment of pilot-tone-aided PNC algorithm guarantees a bit error rate (BER) performance below the forward error correction (FEC) threshold. Moreover, we also evaluate a novel digital signal processing (DSP) algorithm for adaptive pilot tone detection.

2x2 MIMO-OFDM Gigabit fiber-wireless access system based on polarization division multiplexed WDM-PON.

We propose a spectral efficient radio over wavelength division multiplexed passive optical network (WDM-PON) system by combining optical polarization division multiplexing (PDM) and wireless multiple input multiple output (MIMO) spatial multiplexing techniques. In our experiment, a training-based zero forcing (ZF) channel estimation algorithm is designed to compensate the polarization rotation and wireless multipath fading. A 797 Mb/s net data rate QPSK-OFDM signal with error free (<1 × 10(5)) performance and a 1.59 Gb/s net data rate 16QAM-OFDM signal with BER performance of 1.2 × 10(2) are achieved after transmission of 22.8 km single mode fiber followed by 3 m and 1 m air distances, respectively.

100 Gbit/s hybrid optical fiber-wireless link in the W-band (75-110 GHz).

We experimentally demonstrate an 100 Gbit/s hybrid optical fiber-wireless link by employing photonic heterodyning up-conversion of optical 12.5 Gbaud polarization multiplexed 16-QAM baseband signal with two free running lasers. Bit-error-rate performance below the FEC limit is successfully achieved for air transmission distances up to 120 cm.

Digital non-linear equalization for flexible capacity ultradense WDM channels for metro core networking.

An experimental demonstration of Ultradense WDM with advanced digital signal processing is presented. The scheme proposed allows the use of independent tunable DFB lasers spaced at 12.5 GHz for ultradense WDM PM-QPSK flexible capacity channels for metro core networking. To allocate extremely closed carriers, we demonstrate that a digital non-linear equalization allow to mitigate inter-channel interference and improve overall system performance in terms of OSNR. Evaluation of the algorithm and comparison with an ultradense WDM system with coherent carriers generated from a single laser are also reported.