Optical communications systems’ performance is limited by physical layer nonlinearities inherent in some of their transmitter components. In order to overcome some of these limitations, several approaches utilizing artificial neural networks to digitally pre-distort the transmitted signal have been proposed in the last few years, alongside other more classical models. However, their performance was strongly dependent on the specific training sequence used. This work improves the performance of neural network-based direct learning approach for digital pre-distortion applications. The proposed method, which is based on the curriculum learning approach, aims to construct a training paradigm of gradually increasing complexity based on the statistical properties of the input signal, in order to achieve better convergence of the network. We prove that the proposed method defines a proper curriculum regardless of the training data distribution, considering the proposed wide-sense curriculum conditions and the case of binary weighting functions. A comparative analysis examines a pre-distorter for a coherent optical transmitter modelled by a Long Short-Term Memory neural network. An improvement of 4.8dB in terms of normalized mean-square error is demonstrated for the case of a 3-bits linear quantizer, compared to the conventional training scheme.

Analog distortion compensation based on digital signal processing methods are widely applied for transmitter or receiver impairments in various digital communication systems. Recently, several neural network methods were developed for distortion compensation. However, while digital communication systems are typically complex-valued, neural networks are mostly designed to work with real-valued inputs. Thus, adaptations of the network architecture or input data should be applied. In this article a method for using a single-input real-valued neural network for digital communication-based complex-valued signals without any modifications to the neural network is proposed. The method transforms the complex-valued signal to a real-valued one by taking the real component of a complex frequency offset applied through digital up-conversion, without affecting the distortion, therefore allowing standard neural network-based functionality with significant reduction in size. The method is tested with a multi-layer perceptron and gated recurrent unit architectures applied over a generic Wiener-Hammerstein model for the case of equalization of a coherent optical system. A reduction of about 7% and 26% in network size is shown for the gated recurrent unit-based and multi-layer perceptron-based architectures respectively, without any significant change in performance. This size reduction capability shows the high potential in applying the proposed method in neural network-based equalization and pre-distortion operations.

The capacity of a discrete noisy channel is achieved when the transmission source is properly matched to the channel, in the sense that the distribution of the transmitted bits maximizes the channel's mutual information. Traditional source matching assumes independency between the source and the channel, while the case of source dependent channel is usually ignored. In this article, the source dependant channel of Optical Code Division Multiple Access (OCDMA), dominated by Multiple Access Interference (MAI) is analyzed. It is shown that source dependency of MAI reverses the expected behavior of the OCDMA channel, and also that for a given value of channel condition, or mutual information, the number of concurrent users can be increased when the source is properly matched to the channel. A simple technique, which allows combining source matching with practical bit stream methods such as synchronization, error correction codes and the utilization of different detection methods, is also presented.