3,817 research outputs found
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
Data quality is a common problem in machine learning, especially in
high-stakes settings such as healthcare. Missing data affects accuracy,
calibration, and feature attribution in complex patterns. Developers often
train models on carefully curated datasets to minimize missing data bias;
however, this reduces the usability of such models in production environments,
such as real-time healthcare records. Making machine learning models robust to
missing data is therefore crucial for practical application. While some
classifiers naturally handle missing data, others, such as deep neural
networks, are not designed for unknown values. We propose a novel neural
network modification to mitigate the impacts of missing data. The approach is
inspired by neuromodulation that is performed by biological neural networks.
Our proposal replaces the fixed weights of a fully-connected layer with a
function of an additional input (reliability score) at each input, mimicking
the ability of cortex to up- and down-weight inputs based on the presence of
other data. The modulation function is jointly learned with the main task using
a multi-layer perceptron. We tested our modulating fully connected layer on
multiple classification, regression, and imputation problems, and it either
improved performance or generated comparable performance to conventional neural
network architectures concatenating reliability to the inputs. Models with
modulating layers were more robust against degradation of data quality by
introducing additional missingness at evaluation time. These results suggest
that explicitly accounting for reduced information quality with a modulating
fully connected layer can enable the deployment of artificial intelligence
systems in real-time settings
Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems
This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method
- …