3,588 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage
Wireless visual sensor networks (VSNs) are expected to play a major role in
future IEEE 802.15.4 personal area networks (PAN) under recently-established
collision-free medium access control (MAC) protocols, such as the IEEE
802.15.4e-2012 MAC. In such environments, the VSN energy consumption is
affected by the number of camera sensors deployed (spatial coverage), as well
as the number of captured video frames out of which each node processes and
transmits data (temporal coverage). In this paper, we explore this aspect for
uniformly-formed VSNs, i.e., networks comprising identical wireless visual
sensor nodes connected to a collection node via a balanced cluster-tree
topology, with each node producing independent identically-distributed
bitstream sizes after processing the video frames captured within each network
activation interval. We derive analytic results for the energy-optimal
spatio-temporal coverage parameters of such VSNs under a-priori known bounds
for the number of frames to process per sensor and the number of nodes to
deploy within each tier of the VSN. Our results are parametric to the
probability density function characterizing the bitstream size produced by each
node and the energy consumption rates of the system of interest. Experimental
results reveal that our analytic results are always within 7% of the energy
consumption measurements for a wide range of settings. In addition, results
obtained via a multimedia subsystem show that the optimal spatio-temporal
settings derived by the proposed framework allow for substantial reduction of
energy consumption in comparison to ad-hoc settings. As such, our analytic
modeling is useful for early-stage studies of possible VSN deployments under
collision-free MAC protocols prior to costly and time-consuming experiments in
the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video
Technology, 201
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Integrated Sensing and Communications: Recent Advances and Ten Open Challenges
It is anticipated that integrated sensing and communications (ISAC) would be
one of the key enablers of next-generation wireless networks (such as beyond 5G
(B5G) and 6G) for supporting a variety of emerging applications. In this paper,
we provide a comprehensive review of the recent advances in ISAC systems, with
a particular focus on their foundations, system design, networking aspects and
ISAC applications. Furthermore, we discuss the corresponding open questions of
the above that emerged in each issue. Hence, we commence with the information
theory of sensing and communications (SC), followed by the
information-theoretic limits of ISAC systems by shedding light on the
fundamental performance metrics. Next, we discuss their clock synchronization
and phase offset problems, the associated Pareto-optimal signaling strategies,
as well as the associated super-resolution ISAC system design. Moreover, we
envision that ISAC ushers in a paradigm shift for the future cellular networks
relying on network sensing, transforming the classic cellular architecture,
cross-layer resource management methods, and transmission protocols. In ISAC
applications, we further highlight the security and privacy issues of wireless
sensing. Finally, we close by studying the recent advances in a representative
ISAC use case, namely the multi-object multi-task (MOMT) recognition problem
using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for
the outstanding contributions of coauthors in the paper
Dynamic Reconfiguration in Camera Networks: A Short Survey
There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration
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