434 research outputs found
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
Cloud-Edge Non-Orthogonal Transmission for Fog Networks with Delayed CSI at the Cloud
In a Fog Radio Access Network (F-RAN), the cloud processor (CP) collects
channel state information (CSI) from the edge nodes (ENs) over fronthaul links.
As a result, the CSI at the cloud is generally affected by an error due to
outdating. In this work, the problem of content delivery based on fronthaul
transmission and edge caching is studied from an information-theoretic
perspective in the high signal-to-noise ratio (SNR) regime. For the set-up
under study, under the assumption of perfect CSI, prior work has shown the
(approximate or exact) optimality of a scheme in which the ENs transmit
information received from the cloud and cached contents over orthogonal
resources. In this work, it is demonstrated that a non-orthogonal transmission
scheme is able to substantially improve the latency performance in the presence
of imperfect CSI at the cloud.Comment: 5 pages, 4 figures, submitte
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
Gradient-based distributed learning in Parameter Server (PS) computing
architectures is subject to random delays due to straggling worker nodes, as
well as to possible communication bottlenecks between PS and workers. Solutions
have been recently proposed to separately address these impairments based on
the ideas of gradient coding, worker grouping, and adaptive worker selection.
This paper provides a unified analysis of these techniques in terms of
wall-clock time, communication, and computation complexity measures.
Furthermore, in order to combine the benefits of gradient coding and grouping
in terms of robustness to stragglers with the communication and computation
load gains of adaptive selection, novel strategies, named Lazily Aggregated
Gradient Coding (LAGC) and Grouped-LAG (G-LAG), are introduced. Analysis and
results show that G-LAG provides the best wall-clock time and communication
performance, while maintaining a low computational cost, for two representative
distributions of the computing times of the worker nodes.Comment: Submitte
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
Neuromorphic hardware platforms, such as Intel's Loihi chip, support the
implementation of Spiking Neural Networks (SNNs) as an energy-efficient
alternative to Artificial Neural Networks (ANNs). SNNs are networks of neurons
with internal analogue dynamics that communicate by means of binary time
series. In this work, a probabilistic model is introduced for a generalized
set-up in which the synaptic time series can take values in an arbitrary
alphabet and are characterized by both causal and instantaneous statistical
dependencies. The model, which can be considered as an extension of exponential
family harmoniums to time series, is introduced by means of a hybrid
directed-undirected graphical representation. Furthermore, distributed learning
rules are derived for Maximum Likelihood and Bayesian criteria under the
assumption of fully observed time series in the training set.Comment: Published in IEEE ICASSP 2019. Author's Accepted Manuscrip
Soft-TTL: Time-Varying Fractional Caching
Standard Time-to-Live (TTL) cache management prescribes the storage of entire
files, or possibly fractions thereof, for a given amount of time after a
request. As a generalization of this approach, this work proposes the storage
of a time-varying, diminishing, fraction of a requested file. Accordingly, the
cache progressively evicts parts of the file over an interval of time following
a request. The strategy, which is referred to as soft-TTL, is justified by the
fact that traffic traces are often characterized by arrival processes that
display a decreasing, but non-negligible, probability of observing a request as
the time elapsed since the last request increases. An optimization-based
analysis of soft-TTL is presented, demonstrating the important role played by
the hazard function of the inter-arrival request process, which measures the
likelihood of observing a request as a function of the time since the most
recent request
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