End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks

We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84 Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel

Machine learning approach for computing optical properties of a photonic crystal fiber

Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared

Time-Domain Learned Digital Back-Propagation

Performance for optical fibre transmissions can be improved by digitally reversing the channel environment. When this is achieved by simulating short segment by separating the chromatic dispersion and Kerr nonlinearity, this is known as digital back-propagation (DBP). Time-domain DBP has the potential to decrease the complexity with respect to frequency domain algorithms. However, when using finer step in the algorithm, the accuracy of the individual smaller steps suffers. By adapting the chromatic dispersion filters of the individual steps to simulated or measured data this problem can be mitigated. Machine learning frameworks have enabled the gradient-descent style adaptation for large algorithms. This allows to adopt many dispersion filters to accurately represent the transmission in reverse. The proposed technique has been used in an experimental demonstration of learned time-domain DBP using a four channel 64-GBd dual-polarization 64-QAM signal transmission over a 10 span recirculating loop totalling 1014 km. The signal processing scheme consists of alternating finite impulse response filters with nonlinear phase shifts, where the filter coefficient were adapted using the experimental measurements. Performance gains to linear compensation in terms of signal-to-noise ratio improvements were comparable to those achieved with conventional frequency-domain DBP. Our experimental investigation shows the potential of digital signal processing techniques with learned parameters in improving the performance of high data rate long-haul optical fibre transmission systems

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In particular, our focus is on the recently proposed sliding window bidirectional RNN (SBRNN) optical fiber auto-encoder. We show that adjusting the processing window in the sequence estimation algorithm at the receiver improves the reach of simple systems trained on a channel model and applied 'as is' to the transmission link. Moreover, the collected experimental data was used to optimize the receiver neural network parameters, allowing to transmit 42Gb/s with bit-error rate (BER) below the 6.7% hard-decision forward error correction threshold at distances up to 70 km as well as 84 Gb/s at 20 km. The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization. Our results show that, for fixed algorithm memory, the DSP based on deep learning achieves an improved BER performance, allowing to increase the reach of the system

Nonlinearity-free coherent transmission in hollow-core antiresonant fiber

We demonstrate the first multiterabit/s wavelength division multiplexing data transmission through hollow-core antiresonant fiber (HC-ARF). In total, 16 channels of 32-GBd dual-polarization Nyquist-shaped 256QAM signal channels were transmitted through a 270-m-long fiber without observing any power penalty. In a single-channel high power transmission experiment, no nonlinearity penalty was observed for up to 1 W of received power, despite the very low chromatic dispersion of the fiber (<2 ps/nm/km). Our simulations show that such a low level of nonlinearity should enable transmission at 6.4 Tb/s over 1200 km of HC-ARF, even when the fiber attenuation is significantly greater than that of SMF-28. As signals propagate through hollow-core fibers at close to the speed of light in vacuum such a link would be of interest in latency-sensitive data transmission applications