73,658 research outputs found
A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems
Deep learning (DL)-based autoencoder is a potential architecture to implement
end-to-end communication systems. In this letter, we first give a brief
introduction to the autoencoder-represented communication system. Then, we
propose a novel generalized data representation (GDR) aiming to improve the
data rate of DL-based communication systems. Finally, simulation results show
that the proposed GDR scheme has lower training complexity, comparable block
error rate performance and higher channel capacity than the conventional
one-hot vector scheme. Furthermore, we investigate the effect of
signal-to-noise ratio (SNR) in DL-based communication systems and prove that
training at a high SNR could produce a good training performance for
autoencoder
Deep Potential: a general representation of a many-body potential energy surface
We present a simple, yet general, end-to-end deep neural network
representation of the potential energy surface for atomic and molecular
systems. This methodology, which we call Deep Potential, is "first-principle"
based, in the sense that no ad hoc approximations or empirical fitting
functions are required. The neural network structure naturally respects the
underlying symmetries of the systems. When tested on a wide variety of
examples, Deep Potential is able to reproduce the original model, whether
empirical or quantum mechanics based, within chemical accuracy. The
computational cost of this new model is not substantially larger than that of
empirical force fields. In addition, the method has promising scalability
properties. This brings us one step closer to being able to carry out molecular
simulations with accuracy comparable to that of quantum mechanics models and
computational cost comparable to that of empirical potentials
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations
Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising
solution to sustain the explosive mobile traffic demand through network
densification. By placing access points, processors, and storage units as close
as possible to mobile users, H-UDNs bring forth a number of advantages,
including high spectral efficiency, high energy efficiency, and low latency.
Nonetheless, the high density and diversity of network entities in H-UDNs
introduce formidable design challenges in collaborative signal processing and
resource management. This article illustrates the great potential of machine
learning techniques in solving these challenges. In particular, we show how to
utilize graphical representations of H-UDNs to design efficient machine
learning algorithms
Design of Communication Systems using Deep Learning: A Variational Inference Perspective
Recent research in the design of end to end communication system using deep
learning has produced models which can outperform traditional communication
schemes. Most of these architectures leveraged autoencoders to design the
encoder at the transmitter and decoder at the receiver and train them jointly
by modeling transmit symbols as latent codes from the encoder. However, in
communication systems, the receiver has to work with noise corrupted versions
of transmit symbols. Traditional autoencoders are not designed to work with
latent codes corrupted with noise. In this work, we provide a framework to
design end to end communication systems which accounts for the existence of
noise corrupted transmit symbols. The proposed method uses deep neural
architecture. An objective function for optimizing these models is derived
based on the concepts of variational inference. Further, domain knowledge such
as channel type can be systematically integrated into the objective. Through
numerical simulation, the proposed method is shown to consistently produce
models with better packing density and achieving it faster in multiple popular
channel models as compared to the previous works leveraging deep learning
models
Online unsupervised deep unfolding for massive MIMO channel estimation
Massive MIMO communication systems have a huge potential both in terms of
data rate and energy efficiency, although channel estimation becomes
challenging for a large number antennas. Using a physical model allows to ease
the problem by injecting a priori information based on the physics of
propagation. However, such a model rests on simplifying assumptions and
requires to know precisely the configuration of the system, which is
unrealistic in practice. In this letter, we propose to perform online learning
for channel estimation in a massive MIMO context, adding flexibility to
physical channel models by unfolding a channel estimation algorithm (matching
pursuit) as a neural network. This leads to a computationally efficient neural
network structure that can be trained online when initialized with an imperfect
model. The method allows a base station to automatically correct its channel
estimation algorithm based on incoming data, without the need for a separate
offline training phase. It is applied to realistic millimeter wave channels and
shows great performance, achieving a channel estimation error almost as low as
one would get with a perfectly calibrated system
Model-free, Model-based, and General Intelligence
During the 60s and 70s, AI researchers explored intuitions about intelligence
by writing programs that displayed intelligent behavior. Many good ideas came
out from this work but programs written by hand were not robust or general.
After the 80s, research increasingly shifted to the development of learners
capable of inferring behavior and functions from experience and data, and
solvers capable of tackling well-defined but intractable models like SAT,
classical planning, Bayesian networks, and POMDPs. The learning approach has
achieved considerable success but results in black boxes that do not have the
flexibility, transparency, and generality of their model-based counterparts.
Model-based approaches, on the other hand, require models and scalable
algorithms. Model-free learners and model-based solvers have close parallels
with Systems 1 and 2 in current theories of the human mind: the first, a fast,
opaque, and inflexible intuitive mind; the second, a slow, transparent, and
flexible analytical mind. In this paper, I review developments in AI and draw
on these theories to discuss the gap between model-free learners and
model-based solvers, a gap that needs to be bridged in order to have
intelligent systems that are robust and general
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
The growing demand of industrial, automotive and service robots presents a
challenge to the centralized Cloud Robotics model in terms of privacy,
security, latency, bandwidth, and reliability. In this paper, we present a `Fog
Robotics' approach to deep robot learning that distributes compute, storage and
networking resources between the Cloud and the Edge in a federated manner. Deep
models are trained on non-private (public) synthetic images in the Cloud; the
models are adapted to the private real images of the environment at the Edge
within a trusted network and subsequently, deployed as a service for
low-latency and secure inference/prediction for other robots in the network. We
apply this approach to surface decluttering, where a mobile robot picks and
sorts objects from a cluttered floor by learning a deep object recognition and
a grasp planning model. Experiments suggest that Fog Robotics can improve
performance by sim-to-real domain adaptation in comparison to exclusively using
Cloud or Edge resources, while reducing the inference cycle time by 4\times to
successfully declutter 86% of objects over 213 attempts.Comment: IEEE International Conference on Robotics and Automation, ICRA, 201
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
The deviation of continuous and discrete complex random variables from the
traditional proper and symmetric assumption to a generalized improper and
asymmetric characterization (accounting correlation between a random entity and
its complex conjugate), respectively, introduces new design freedom and various
potential merits. As such, the theory of impropriety has vast applications in
medicine, geology, acoustics, optics, image and pattern recognition, computer
vision, and other numerous research fields with our main focus on the
communication systems. The journey begins from the design of improper Gaussian
signaling in the interference-limited communications and leads to a more
elaborate and practically feasible asymmetric discrete modulation design. Such
asymmetric shaping bridges the gap between theoretically and practically
achievable limits with sophisticated transceiver and detection schemes in both
coded/uncoded wireless/optical communication systems. Interestingly,
introducing asymmetry and adjusting the transmission parameters according to
some design criterion render optimal performance without affecting the
bandwidth or power requirements of the systems. This dual-flavored article
initially presents the tutorial base content covering the interplay of
reality/complexity, propriety/impropriety and circularity/noncircularity and
then surveys majority of the contributions in this enormous journey.Comment: IEEE COMST (Early Access
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks
Short-term road traffic prediction (STTP) is one of the most important
modules in Intelligent Transportation Systems (ITS). However, network-level
STTP still remains challenging due to the difficulties both in modeling the
diverse traffic patterns and tacking high-dimensional time series with low
latency. Therefore, a framework combining with a deep clustering (DeepCluster)
module is developed for STTP at largescale networks in this paper. The
DeepCluster module is proposed to supervise the representation learning in a
visualized way from the large unlabeled dataset. More specifically, to fully
exploit the traffic periodicity, the raw series is first split into a number of
sub-series for triplets generation. The convolutional neural networks (CNNs)
with triplet loss are utilized to extract the features of shape by transferring
the series into visual images. The shape-based representations are then used
for road segments clustering. Thereafter, motivated by the fact that the road
segments in a group have similar patterns, a model sharing strategy is further
proposed to build recurrent NNs (RNNs)-based predictions through a group-based
model (GM), instead of individual-based model (IM) in which one model are built
for one road exclusively. Our framework can not only significantly reduce the
number of models and cost, but also increase the number of training data and
the diversity of samples. In the end, we evaluate the proposed framework over
the network of Liuli Bridge in Beijing. Experimental results show that the
DeepCluster can effectively cluster the road segments and GM can achieve
comparable performance against the IM with less number of models.Comment: 12 pages, 15 figures, journa
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