40,085 research outputs found
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
Heterogeneous delays making parents synchronized: A coupled maps on Cayley tree model
We study the phase synchronized clusters in the diffusively coupled maps on
the Cayley tree networks for heterogeneous delay values. Cayley tree networks
comprise of two parts: the inner nodes and the boundary nodes. We find that
heterogeneous delays lead to various cluster states, such as; (a) cluster state
consisting of inner nodes and boundary nodes, and (b) cluster state consisting
of only boundary nodes. The former state may comprise of nodes from all the
generations forming self-organized cluster or nodes from few generations
yielding driven clusters depending upon on the parity of heterogeneous delay
values. Furthermore, heterogeneity in delays leads to the lag synchronization
between the siblings lying on the boundary by destroying the exact
synchronization among them. The time lag being equal to the difference in the
delay values. The Lyapunov function analysis sheds light on the destruction of
the exact synchrony among the last generation nodes. To the end we discuss the
relevance of our results with respect to their applications in the family
business as well as in understanding the occurrence of genetic diseases.Comment: 9 pages, 11 figure
Holographic and 3D teleconferencing and visualization: implications for terabit networked applications
Abstract not available
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling
This paper investigates the dependence of synchronization transitions of
bursting oscillations on the information transmission delay over scale-free
neuronal networks with attractive and repulsive coupling. It is shown that for
both types of coupling, the delay always plays a subtle role in either
promoting or impairing synchronization. In particular, depending on the
inherent oscillation period of individual neurons, regions of irregular and
regular propagating excitatory fronts appear intermittently as the delay
increases. These delay-induced synchronization transitions are manifested as
well-expressed minima in the measure for spatiotemporal synchrony. For
attractive coupling, the minima appear at every integer multiple of the average
oscillation period, while for the repulsive coupling, they appear at every odd
multiple of the half of the average oscillation period. The obtained results
are robust to the variations of the dynamics of individual neurons, the system
size, and the neuronal firing type. Hence, they can be used to characterize
attractively or repulsively coupled scale-free neuronal networks with delays.Comment: 15 pages, 9 figures; accepted for publication in PLoS ONE [related
work available at http://arxiv.org/abs/0907.4961 and
http://www.matjazperc.com/
Low-dimensional behavior of Kuramoto model with inertia in complex networks
Low-dimensional behavior of large systems of globally coupled oscillators has
been intensively investigated since the introduction of the Ott-Antonsen
ansatz. In this report, we generalize the Ott-Antonsen ansatz to second-order
Kuramoto models in complex networks. With an additional inertia term, we find a
low-dimensional behavior similar to the first-order Kuramoto model, derive a
self-consistent equation and seek the time-dependent derivation of the order
parameter. Numerical simulations are also conducted to verify our analytical
results.Comment: 6 figure
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