18,681 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions
In this article, we study the nonlinear Fokker-Planck (FP) equation that
arises as a mean-field (macroscopic) approximation of bounded confidence
opinion dynamics, where opinions are influenced by environmental noises and
opinions of radicals (stubborn individuals). The distribution of radical
opinions serves as an infinite-dimensional exogenous input to the FP equation,
visibly influencing the steady opinion profile. We establish mathematical
properties of the FP equation. In particular, we (i) show the well-posedness of
the dynamic equation, (ii) provide existence result accompanied by a
quantitative global estimate for the corresponding stationary solution, and
(iii) establish an explicit lower bound on the noise level that guarantees
exponential convergence of the dynamics to stationary state. Combining the
results in (ii) and (iii) readily yields the input-output stability of the
system for sufficiently large noises. Next, using Fourier analysis, the
structure of opinion clusters under the uniform initial distribution is
examined. Specifically, two numerical schemes for identification of
order-disorder transition and characterization of initial clustering behavior
are provided. The results of analysis are validated through several numerical
simulations of the continuum-agent model (partial differential equation) and
the corresponding discrete-agent model (interacting stochastic differential
equations) for a particular distribution of radicals
Analysis of the Efficiency PETSc and PETIGA Libraries in Solving the Problem of Crystal Growth
We present an analysis of high performance computational method for solving the problem of crystal grows. The method uses PETSc and PETIGA C-language based libraries and supports parallel computing. The evolution of calculation process was studied in series of special computations are obtained on innovative mobile cluster platform, which provides exclusive system tuning abilities. The results of research confirm the high efficiency of the proposed algorithm on multi-core computer systems and allow us to recommend the use of PETSc and PETIGA for solving high order differential equations
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
A Practical Guide to Surface Kinetic Monte Carlo Simulations
This review article is intended as a practical guide for newcomers to the
field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC
simulations as prevalently used for surface and interface applications. We will
provide worked out examples using the kmos code, where we highlight the central
approximations made in implementing a KMC model as well as possible pitfalls.
This includes the mapping of the problem onto a lattice and the derivation of
rate constant expressions for various elementary processes. Example KMC models
will be presented within the application areas surface diffusion, crystal
growth and heterogeneous catalysis, covering both transient and steady-state
kinetics as well as the preparation of various initial states of the system. We
highlight the sensitivity of KMC models to the elementary processes included,
as well as to possible errors in the rate constants. For catalysis models in
particular, a recurrent challenge is the occurrence of processes at very
different timescales, e.g. fast diffusion processes and slow chemical
reactions. We demonstrate how to overcome this timescale disparity problem
using recently developed acceleration algorithms. Finally, we will discuss how
to account for lateral interactions between the species adsorbed to the
lattice, which can play an important role in all application areas covered
here.Comment: This document is the final Author's version of a manuscript that has
been peer reviewed and accepted for publication in Frontiers in Chemistry. To
access the final edited and published work see
https://www.frontiersin.org/articles/10.3389/fchem.2019.00202/abstrac
Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network
The limited fronthaul capacity imposes a challenge on the uplink of
centralized radio access network (C-RAN). We propose to boost the fronthaul
capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by
globally optimizing the power sharing between channel estimation and data
transmission both for the user devices (UDs) and the remote radio units (RRUs).
Intuitively, allocating more power to the channel estimation will result in
more accurate channel estimates, which increases the achievable throughput.
However, increasing the power allocated to the pilot training will reduce the
power assigned to data transmission, which reduces the achievable throughput.
In order to optimize the powers allocated to the pilot training and to the data
transmission of both the UDs and the RRUs, we assign an individual power
sharing factor to each of them and derive an asymptotic closed-form expression
of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN
consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU)
links. We then exploit the C-RAN architecture's central computing and control
capability for jointly optimizing the UDs' power sharing factors and the RRUs'
power sharing factors aiming for maximizing the fronthaul capacity. Our
simulation results show that the fronthaul capacity is significantly boosted by
the proposed global optimization of the power allocation between channel
estimation and data transmission both for the UDs and for their host RRUs. As a
specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs
deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing
factors improves 33\% compared with the one attained without optimizing power
sharing factors
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