Marcos Álvarez-Díaz

SNR Estimation for Multilevel Constellations Using Higher-Order Moments

The performance of existing moments-based non-data-aided (NDA) estimators of signal-to-noise ratio (SNR) in digital communication systems substantially degrades with multilevel constellations. We propose a novel moments-based approach that is amenable to practical implementation and significantly improves on previous estimators of this class. This approach is based on a linear combination of ratios of certain even-order moments, which allow the derivation of NDA SNR estimators without requiring memory-costly lookup tables. The weights of the linear combination can be tuned according to the constellation and the SNR operation range. As particular case we develop an eighth-order statistics (EOS)-based estimator, showing in detail the statistical analysis that leads to the weight optimization procedure. The EOS-based estimators yield improved performance for multilevel constellations, especially for those with two and three amplitude levels. Monte Carlo simulations validate the new approach in a wide SNR range.

Forecasting exchange rates using genetic algorithms

A novel approach is employed to investigate the predictability of weekly data on the euro/dollar, British pound/dollar, Deutsche mark/dollar, Japanese yen/dollar, French franc/dollar and Canadian dollar/dollar exchange rates. A functional search procedure based on the Darwinian theories of natural evolution and survival, called genetic algorithms (hereinafter GA), was used to find an analytical function that best approximates the time variability of the studied exchange rates. In all cases, the mathematical models found by the GA predict slightly better than the random walk model. The models are heavily dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small. In consequence, the results agree with previous works establishing explicitly that nonlinear nature of exchange rates cannot be exploited to substantially improve forecasting.

Forecasting British Tourist Arrivals in the Balearic Islands Using Meteorological Variables

This paper investigates the possibility of improving the predictive ability of a tourism demand model with meteorological explanatory variables. The authors use as a case study the monthly British tourism demand for the Balearic Islands (Spain). For this purpose, a transfer function model and causal artificial neural network are fitted. The results are compared with those obtained by non-causal methods: an ARIMA model and an autoregressive neural network. The results indicate that incorporating meteorological variables can increase predictive power, although the most accurate prediction is obtained using a non-causal model – specifically, an autoregressive neural network.

Multiantenna detection of multicarrier primary signals exploiting spectral a priori information

We consider the problem of detecting a primary signal over a wireless channel by a multiantenna cognitive spectral monitor with knowledge of the spectral shape of primary transmissions employing multicarrier modulation. As a starting point, a Locally Most Powerful (LMP) test is derived for the single-antenna case. The asymptotic performance improvement of the LMP detector over the standard Energy Detector (ED) is quantified in terms of the primary spectral mask. For the case of an ideal bandpass mask the LMP test is Uniformly Most Powerful. With multiple antennas, optimal detectors require channel knowledge and therefore are not well suited to practical implementation. We propose a realizable multiantenna Incoherent Detector, and compare its performance with that of the ED and LMP single-antenna tests under both deterministic and Rayleigh slow-fading channel scenarios. In the latter setting, multiple antennas introduce detection diversity that determines the slope of the probability of miss curve, and thus the overall detection performance.

Forecasting tourist arrivals to Balearic Islands using genetic programming

Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.

Joint Precoding and Predistortion Techniques for Satellite Telecommunication Systems

We analyze the application of Tomlinson-Harashima precoding to satellite transmission. Precoding counteracts channel dispersion at the price of increasing the dynamic range of the signal to be transmitted. This conflicts with the requirements of typical satellite transmission, where amplifiers with nonlinear characteristics are used, and their incoming signal is desired to present a reduced dynamic range. We propose fractional predistortion as a valid countermeasure against the effects of the nonlinear amplifier. Simulations allow us to obtain the performance of the proposed scheme when applied to two typical satellite propagation environments and compare it to an equivalent scheme using standard decision-feedback equalization. We conclude that a system using predistortion is more sensitive to the effects of satellite transmission-related nonlinearities than the equivalent DFE system

Forecasting US consumer price index: does nonlinearity matter?

ABSTRACT The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.

Diamond contour-based phase recovery for (CROSS)-QAM constellations

Two new iterative methods for blind phase estimation of QAM signals are presented. The algorithms seek the maxima of certain cost functions derived from the dispersion of the derotated data with respect to a diamond-shaped contour, and their distinctive feature is the inclusion of a sign nonlinearity at every iteration. A connection between one of these methods and the standard fourth-power method is presented. Simulations with cross-QAM constellations show that, when properly initialized, the new schemes present lower variance than the fourth-power method, and may even outperform Cartwrights eighth-order method

Research Note: Estimating Price and Income Demand Elasticities for Spain Separately by the Major Source Markets

The main goal of this study is to estimate the price and income elasticity of demand for tourism to Spain. This estimation is done separately for the major international source markets for Spain: Germany, the UK, Italy and the Netherlands. For this purpose, the authors use the autoregressive distributed lag (ARDL) approach to cointegration and the bootstrap method to construct empirical confidence intervals for each estimate. The results reveal that the tourism demand in all the countries studied has a similar income elasticity, which is approximately unitary. However, there is an important difference with regard to price elasticity: tourism demand from the UK is statistically price inelastic, but demand is elastic for the remaining countries. This finding is relevant because, first, it underlines the importance of studying the source markets separately instead of analysing an aggregate international tourism demand, and, second, it supports the need to implement different tourism policies and strategies with respect to the pricing decisions for each source market.

Detecting the socioeconomic driving forces of the fire catastrophe in NW Spain

Wildfires cause devastating environmental, social and economic effects in different regions of the world. The aim of this study was to analyze the long-run relationship between the number of ignition events and socioeconomic variables using time series data. We focus on Galicia, a region in the northwest part of the Iberian Peninsula and with one of the highest fire density and largest burned areas in Europe. Since the late 1980s, the number of forest fires has increased in Galicia and caused extensive damage to the environment, property and human life. The analysis is based on cointegration tests between variables. In order to avoid the problems related to spurious regression, the ARDL bounds testing approach was applied. The statistical evidence allows us to conclude that in the long term, the price of eucalyptus timber, the population in the primary sector and the intensity of the elections are relevant factors in explaining the start of forest fires. These three variables are found to increase the propensity of the population to start a fire that cause devastating environmental, social and economic effects.