1,918 research outputs found
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Prediction-based techniques for the optimization of mobile networks
Mención Internacional en el tÃtulo de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition
of several random phenomena, most of which, related to human activities, such as mobility,
communications and network usage. However, when observed in their totality, the many individual
components merge into more deterministic patterns and trends start to be identifiable and
predictable.
In this thesis we analyze a recent branch of network optimization that is commonly referred to
as anticipatory networking and that entails the combination of prediction solutions and network
optimization schemes. The main intuition behind anticipatory networking is that knowing in
advance what is going on in the network can help understanding potentially severe problems and
mitigate their impact by applying solution when they are still in their initial states. Conversely,
network forecast might also indicate a future improvement in the overall network condition (i.e.
load reduction or better signal quality reported from users). In such a case, resources can be
assigned more sparingly requiring users to rely on buffered information while waiting for the
better condition when it will be more convenient to grant more resources.
In the beginning of this thesis we will survey the current anticipatory networking panorama
and the many prediction and optimization solutions proposed so far. In the main body of the work,
we will propose our novel solutions to the problem, the tools and methodologies we designed to
evaluate them and to perform a real world evaluation of our schemes.
By the end of this work it will be clear that not only is anticipatory networking a very promising
theoretical framework, but also that it is feasible and it can deliver substantial benefit to current
and next generation mobile networks. In fact, with both our theoretical and practical results we
show evidences that more than one third of the resources can be saved and even larger gain can
be achieved for data rate enhancements.Programa Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge OrtÃn Gracia.- Vocal: Guevara Noubi
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