20,947 research outputs found
Control-data separation architecture for cellular radio access networks: a survey and outlook
Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided
Software tool for optimising indoor/outdoor coverage in a construction site
A system architecture, an empirical propagation model, and a software combined with a CAD tool, have been designed to offer mobile communication services to construction sites. Results have been validated by measurements.Peer ReviewedPostprint (published version
Demonstration of detuned dual recycling at the Garching 30m laser interferometer
Dual recycling is an advanced optical technique to enhance the
signal-to-noise ratio of laser interferometric gravitational wave detectors in
a limited bandwidth. To optimise the center of this band with respect to
Fourier frequencies of expected gravitational wave signals detuned dual
recycling has to be implemented. We demonstrated detuned dual recycling on a
fully suspended 30m prototype interferometer. A control scheme that allows to
tune the detector to different frequencies will be outlined. Good agreement
between the experimental results and numerical simulations has been achieved.Comment: 9 page
Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings
Buildings are key actors of the electrical gird. As such they have an important role to play in grid
stabilization, especially in a context where renewable energies are mandated to become an increasingly
important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical
demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate
more efficiently. One of the ways to obtain flexibility from building managers and building users is the
introduction of variable energy prices which evolve depending on the expected load and energy generation.
In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of
consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper,
a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random
Forest machine learning algorithm.This paper is part of a project that has received funding
from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No
768614. This paper reflects only the author´s views and
neither the Agency nor the Commission are responsible
for any use that may be made of the information contained
therein
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
- …