5,242 research outputs found
Electromagnetic Lens-focusing Antenna Enabled Massive MIMO: Performance Improvement and Cost Reduction
Massive multiple-input multiple-output (MIMO) techniques have been recently
advanced to tremendously improve the performance of wireless communication
networks. However, the use of very large antenna arrays at the base stations
(BSs) brings new issues, such as the significantly increased hardware and
signal processing costs. In order to reap the enormous gain of massive MIMO and
yet reduce its cost to an affordable level, this paper proposes a novel system
design by integrating an electromagnetic (EM) lens with the large antenna
array, termed the EM-lens enabled MIMO. The EM lens has the capability of
focusing the power of an incident wave to a small area of the antenna array,
while the location of the focal area varies with the angle of arrival (AoA) of
the wave. Therefore, in practical scenarios where the arriving signals from
geographically separated users have different AoAs, the EM-lens enabled system
provides two new benefits, namely energy focusing and spatial interference
rejection. By taking into account the effects of imperfect channel estimation
via pilot-assisted training, in this paper we analytically show that the
average received signal-to-noise ratio (SNR) in both the single-user and
multiuser uplink transmissions can be strictly improved by the EM-lens enabled
system. Furthermore, we demonstrate that the proposed design makes it possible
to considerably reduce the hardware and signal processing costs with only
slight degradations in performance. To this end, two complexity/cost reduction
schemes are proposed, which are small-MIMO processing with parallel receiver
filtering applied over subgroups of antennas to reduce the computational
complexity, and channel covariance based antenna selection to reduce the
required number of radio frequency (RF) chains. Numerical results are provided
to corroborate our analysis.Comment: 30 pages, 9 figure
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The usefulness of econometric models with stochastic volatility and long memory: Applications for macroeconomic and financial time series
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior.
A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model.
The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries
Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study
Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH speci¯cation of Ding, Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight countries. We ¯nd this multivariate speci¯cation to be generally applicable once power, leverage and long-memory e®ects are taken into consideration. In addition, we ¯nd that both the optimal fractional di®erencing parameter and power transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the multivariate FIAPARCH framework is provided in terms of forecast error statistics and tests for equal forecast accuracy of the various models.Asymmetric Power ARCH, Fractional integration, Stock returns, Volatility forecast evaluation
Continuum Model for Traffic Flow considering Safe Driving Awareness Heterogeneity
This paper defines the concepts of region representative vehicle and driver and region representative safe driving awareness and its heterogeneity, and, based on these concepts and a new car-following model proposed, it proposes a new continuum model for traffic flow considering region representative safe driving awareness heterogeneity. Analyses show that the new continuum model follows traffic flow anisotropy principle, and the following insights can be gotten: (1) the bigger the difference of the preceding region representative safe driving awareness coefficient minus the following region representative safe driving awareness coefficient is, the less the probability of the wrong-way travel (the negative velocity) problem in the new continuum model is; (2) when the preceding region representative safe driving awareness coefficient is not less than the following region representative safe driving awareness coefficient, there is no wrong-way travel problem in the new continuum model, and vice versa
Non-Relativistic Limit of Dirac Equations in Gravitational Field and Quantum Effects of Gravity
Based on unified theory of electromagnetic interactions and gravitational
interactions, the non-relativistic limit of the equation of motion of a charged
Dirac particle in gravitational field is studied. From the Schrodinger equation
obtained from this non-relativistic limit, we could see that the classical
Newtonian gravitational potential appears as a part of the potential in the
Schrodinger equation, which can explain the gravitational phase effects found
in COW experiments. And because of this Newtonian gravitational potential, a
quantum particle in earth's gravitational field may form a gravitationally
bound quantized state, which had already been detected in experiments. Three
different kinds of phase effects related to gravitational interactions are
discussed in this paper, and these phase effects should be observable in some
astrophysical processes. Besides, there exists direct coupling between
gravitomagnetic field and quantum spin, radiation caused by this coupling can
be used to directly determine the gravitomagnetic field on the surface of a
star.Comment: 12 pages, no figur
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