What Can Machine Learning Teach Us about Communications?

Abstract

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well.In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

“…Training of DNN-based DBP. While the concept of DNN-based DBP has been studied in the literature 15,16 , we propose several necessary modifications to enable performance gain in practical transmission experiments using DNN-based DBP. The input to the DNN-based DBP is derived from the coherently detected signal with sampling rate of 2 samples/symbol.…”

“…For single-carrier systems, Kamalov et al 14 conducted a field-trial demonstration using neural networks with information symbol triplets as inputs, but the performance is inferior to standard DBP. On the other hand, Häger and Pfister [15][16][17] considered the linear and nonlinear steps of DBP as a deep neural network (DNN) where preliminary simulation studies for single-channel single-polarization systems are presented. However, practical transmission impairments, such as laser-phase noise, laser-frequency offsets, polarization, and WDM effects, have not been studied.…”

“…Here, we show a unique example of how ML can not only produce readily implementable algorithms advancing system performance, but also complement analytical thinking to develop deeper mathematical insights into fiber nonlinearity compensation. We first demonstrate performance gain in realistic experimental settings using the deep neural network-based digital backpropagation (DNN-based DBP) architecture [15][16][17] . To do so, we take into account the time series and dispersive nature of the received signals, and appropriately integrate the DNN with other essential non-ML DSP blocks.…”

Autoregressive Belief Propagation for Decoding Block Codes

“…Deep learning have been applied to various tasks in communication [1], [2], such as modulation [3], [4], equalization [5], [6], and MIMO detection [7], [8]. Deep learning techniques were also applied successfully to error correcting codes, for example, encoding [9], decoding [10]- [16] and even designing new codes [17] that outperform the state of the art codes for feedback channels [18], [19].…”

Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

“…By viewing all chromatic-dispersion steps as general linear functions, one obtains a parameterized model similar to a multi-layer NN [8]. Compared to standard "black-box" models, this approach has several advantages: it leads to clear hyperparameter choices (such as the number of layers/steps); it provides good initializations for a gradient-based optimization; and it allows one to inspect the learned solutions in order to understand why they work well, thereby providing significant insight into the problem [8][9][10].…”