16,854 research outputs found
5G 3GPP-like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments
For the development of new 5G systems to operate in bands up to 100 GHz,
there is a need for accurate radio propagation models at these bands that
currently are not addressed by existing channel models developed for bands
below 6 GHz. This document presents a preliminary overview of 5G channel models
for bands up to 100 GHz. These have been derived based on extensive measurement
and ray tracing results across a multitude of frequencies from 6 GHz to 100
GHz, and this document describes an initial 3D channel model which includes: 1)
typical deployment scenarios for urban microcells (UMi) and urban macrocells
(UMa), and 2) a baseline model for incorporating path loss, shadow fading, line
of sight probability, penetration and blockage models for the typical
scenarios. Various processing methodologies such as clustering and antenna
decoupling algorithms are also presented.Comment: To be published in 2016 IEEE 83rd Vehicular Technology Conference
Spring (VTC 2016-Spring), Nanjing, China, May 201
A test of analog-based tools for quantitative prediction of large-scale fluvial architecture
Outcrop analogs are routinely used to constrain models of subsurface fluvial sedimentary architecture built through stochastic modeling or inter-well sandbody correlations. Correlability models are analog-based quantitative templates for guiding the well-to-well correlation of sand-bodies, whereas indicator variograms used as input to reservoir models can be parameterized from data collected from analogs, using existing empirical relationships. This study tests the value and limitations of adopting analog-informed correlability models and indicator-variogram models, and assesses the impact and significance of analog choice in subsurface workflows for characterizing fluvial reservoirs. A 3.2 km long architectural panel based on a Virtual Outcrop from the Cretaceous Blackhawk Formation (Wasatch Plateau, Utah, USA) has been used to test the methodologies: vertical 'dummy' wells have been constructed across the panel, and the intervening fluvial architecture has been predicted using correlability models and sequential indicator simulations. The correlability and indicator-variogram models employed to predict the outcrop architecture have been compiled using information drawn from an architectural database. These models relate to: (i) analogs that partially match with the Blackhawk Formation in terms of depositional setting, and (ii) empirical relationships relating statistics on depositional-element geometries and spatial relations to net-to-gross ratio, based on data from multiple fluvial systems of a variety of forms. The forecasting methods are assessed by quantifying the mismatch between predicted architecture and outcrop observations in terms of the correlability of channel complexes and static connectivity of channel deposits. Results highlight the effectiveness of correlability models as a check for the geologic realism of correlation panels, and the value of analog-informed indicator variograms as a valid alternative to variogram-model parameterization through geostatistical analysis of well data. This work has application in the definition of best-practice use of analogs in subsurface workflows; it provides insight into the typical degree of realism of analog-based predictions of reservoir architecture, as well as on the impact of analog choice, and draws attention to associated pitfalls
Approximation of empowerment in the continuous domain
The empowerment formalism offers a goal-independent utility function fully derived from an agent's embodiment. It produces intrinsic motivations which can be used to generate self-organizing behaviours in agents. One obstacle to the application of empowerment in more demanding (esp. continuous) domains is that previous ways of calculating empowerment have been very time consuming and only provided a proof-of-concept. In this paper we present a new approach to efficiently approximate empowerment as a parallel, linear, Gaussian channel capacity problem. We use pendulum balancing to demonstrate this new method, and compare it to earlier approximation methods.Peer reviewe
MIMO Channel Correlation in General Scattering Environments
This paper presents an analytical model for the fading channel correlation in
general scattering environments. In contrast to the existing correlation
models, our new approach treats the scattering environment as non-separable and
it is modeled using a bi-angular power distribution. The bi-angular power
distribution is parameterized by the mean departure and arrival angles, angular
spreads of the univariate angular power distributions at the transmitter and
receiver apertures, and a third parameter, the covariance between transmit and
receive angles which captures the statistical interdependency between angular
power distributions at the transmitter and receiver apertures. When this third
parameter is zero, this new model reduces to the well known "Kronecker" model.
Using the proposed model, we show that Kronecker model is a good approximation
to the actual channel when the scattering channel consists of a single
scattering cluster. In the presence of multiple remote scattering clusters we
show that Kronecker model over estimates the performance by artificially
increasing the number of multipaths in the channel.Comment: Australian Communication Theory Workshop Proceedings 2006, Perth
Western Australia. (accepted
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Reconstruction of three-dimensional porous media using generative adversarial neural networks
To evaluate the variability of multi-phase flow properties of porous media at
the pore scale, it is necessary to acquire a number of representative samples
of the void-solid structure. While modern x-ray computer tomography has made it
possible to extract three-dimensional images of the pore space, assessment of
the variability in the inherent material properties is often experimentally not
feasible. We present a novel method to reconstruct the solid-void structure of
porous media by applying a generative neural network that allows an implicit
description of the probability distribution represented by three-dimensional
image datasets. We show, by using an adversarial learning approach for neural
networks, that this method of unsupervised learning is able to generate
representative samples of porous media that honor their statistics. We
successfully compare measures of pore morphology, such as the Euler
characteristic, two-point statistics and directional single-phase permeability
of synthetic realizations with the calculated properties of a bead pack, Berea
sandstone, and Ketton limestone. Results show that GANs can be used to
reconstruct high-resolution three-dimensional images of porous media at
different scales that are representative of the morphology of the images used
to train the neural network. The fully convolutional nature of the trained
neural network allows the generation of large samples while maintaining
computational efficiency. Compared to classical stochastic methods of image
reconstruction, the implicit representation of the learned data distribution
can be stored and reused to generate multiple realizations of the pore
structure very rapidly.Comment: 21 pages, 20 figure
Antenna array calibration in wireless communications
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