274,223 research outputs found
Online Resource Inference in Network Utility Maximization Problems
The amount of transmitted data in computer networks is expected to grow
considerably in the future, putting more and more pressure on the network
infrastructures. In order to guarantee a good service, it then becomes
fundamental to use the network resources efficiently. Network Utility
Maximization (NUM) provides a framework to optimize the rate allocation when
network resources are limited. Unfortunately, in the scenario where the amount
of available resources is not known a priori, classical NUM solving methods do
not offer a viable solution. To overcome this limitation we design an overlay
rate allocation scheme that attempts to infer the actual amount of available
network resources while coordinating the users rate allocation. Due to the
general and complex model assumed for the congestion measurements, a passive
learning of the available resources would not lead to satisfying performance.
The coordination scheme must then perform active learning in order to speed up
the resources estimation and quickly increase the system performance. By
adopting an optimal learning formulation we are able to balance the tradeoff
between an accurate estimation, and an effective resources exploitation in
order to maximize the long term quality of the service delivered to the users
Model-Centric and Data-Centric Aspects of Active Learning for Neural Network Models
We study different data-centric and model-centric aspects of active learning
with neural network models. i) We investigate incremental and cumulative
training modes that specify how the currently labeled data are used for
training. ii) Neural networks are models with a large capacity. Thus, we study
how active learning depends on the number of epochs and neurons as well as the
choice of batch size. iii) We analyze in detail the behavior of query
strategies and their corresponding informativeness measures and accordingly
propose more efficient querying and active learning paradigms. iv) We perform
statistical analyses, e.g., on actively learned classes and test error
estimation, that reveal several insights about active learning
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications
Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave
(mmWave) and terahertz (THz) systems to achieve both coverage and capacity
enhancement, where the design of hybrid precoders, combiners, and the IRS
typically relies on channel state information. In this paper, we address the
problem of uplink wideband channel estimation for IRS aided multiuser
multiple-input single-output (MISO) systems with hybrid architectures.
Combining the structure of model driven and data driven deep learning
approaches, a hybrid driven learning architecture is devised for joint
estimation and learning the properties of the channels. For a passive IRS aided
system, we propose a residual learned approximate message passing as a model
driven network. A denoising and attention network in the data driven network is
used to jointly learn spatial and frequency features. Furthermore, we design a
flexible hybrid driven network in a hybrid passive and active IRS aided system.
Specifically, the depthwise separable convolution is applied to the data driven
network, leading to less network complexity and fewer parameters at the IRS
side. Numerical results indicate that in both systems, the proposed hybrid
driven channel estimation methods significantly outperform existing deep
learning-based schemes and effectively reduce the pilot overhead by about 60%
in IRS aided systems.Comment: 30 pages, 8 figures, submitted to IEEE transactions on wireless
communications on December 13, 202
Reducing probes for quality of transmission estimation in optical networks with active learning
Estimating the quality of transmission (QoT) of a lightpath before its
establishment is a critical procedure for efficient design and
management of optical networks. Recently, supervised machine learning
(ML) techniques for QoT estimation have been proposed as an effective
alternative to well-established, yet approximated, analytic models
that often require the introduction of conservative margins to
compensate for model inaccuracies and uncertainties. Unfortunately, to
ensure high estimation accuracy, the training set (i.e., the set of
historical field data, or "samples," required to train these
supervised ML algorithms) must be very large, while in real network
deployments, the number of monitored/monitorable lightpaths is limited
by several practical considerations. This is especially true for
lightpaths with an above-threshold bit error rate (BER) (i.e.,
malfunctioning or wrongly dimensioned lightpaths), which are
infrequently observed during network operation. Samples with
above-threshold BERs can be acquired by deploying probe lightpaths,
but at the cost of increased operational expenditures and wastage of
spectral resources. In this paper, we propose to use active learning to reduce the number of probes
needed for ML-based QoT estimation. We build an estimation model based
on Gaussian processes, which allows iterative identification of those
QoT instances that minimize estimation uncertainty. Numerical results
using synthetically generated datasets show that, by using the
proposed active learning approach, we can achieve the same performance
of standard offline supervised ML methods, but with a remarkable
reduction (at least 5% and up to 75%) in the number of training
samples
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