An OFDM Signal Identification Method for Wireless Communications Systems

Distinction of OFDM signals from single carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed signals vanish in contrary to the cumulants of other signals. Thus fourth order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques

Multidimensional Signal Analysis for Wireless Communications Systems

Wireless communications systems underwent an evolution as the voice oriented applications evolved to data and multimedia based services. Furthermore, current wireless technologies, regulations and the un- derstanding of the technology are insufficient for the requirements of future wireless systems. Along with the rapid rise at the number of users, increasing demand for more communications capacity to deploy multimedia applications entail effective utilization of communications resources. Therefore, there is a need for effective spectrum allocation, adaptive and complex modulation, error recovery, channel estimation, diversity and code design techniques to allow high data rates while maintaining desired quality of service, and reconfigurable and flexible air interface technologies for better interference and fading management. However, traditional communications system design is based on allocating fixed amounts of resources to the user and does not consider adaptive spectrum utilization. Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to come up with consistent methodologies to provide solutions for the capacity, interference, and reliability problems of the wireless networks. Spectrum sensing feature of cognitive radio systems are a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to achieve the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro hyperspace. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing a mechanism, not only as a part of CR systems which provide channel occupancy information but also as a communication environment awareness component of dynamic spectrum access paradigm which can adapt sensing parameters autonomously to ensure robust identification and parameter estimation for the signals over the monitored spectrum. Such an approach will lead to recognition of communications opportunities in different dimensions of spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish adaptive resource management and spectrum access. We define multidimensional signal analysis as a methodology, which not only provides the information that the spectrum hyperspace dimension in interest is occupied or not, but also reveals the underlaying information regarding to the parameters, such as employed channel access methods, duplexing techniques and other parameters related to the air interfaces of the signals accessing to the monitored channels and more. To achieve multidimensional signal analysis, a comprehensive sensing, classification, and a detection approach is required at the initial stage. In this thesis, we propose the multidimensional signal analysis procedures under signal identification algorithms in time, frequency. Moreover, an angle of arrival estimation system for wireless signals, and a spectrum usage modeling and prediction method are proposed as multidimensional signal analysis functionalities

Overweight and obesity in preschool children in Turkey: A multilevel analysis

Childhood obesity/overweight is a worldwide concern and its prevalence is increasing in many countries. The first aim of this study is to analyse the trends in overweight and obesity among children under the age of five in Turkey based on the new World Health Organization (WHO) standards, using data from the 'five-round of the Turkey Demographic and Health Surveys' (TDHSs). The second aim is to examine whether or not the maternal/household and individual-level factors are associated with overweight/obesity using TDHS 2003, 2008, and 2013 datasets. A total sample of 14,231 children under the age of five were extracted from the TDHS in 1993, 1998, 2003, 2008, and 2013 to determine the prevalence of the trend. Pooled data from 8,812 children were included in the analysis to examine factors associated with overweight/obesity. Taking into account the clustered data structure, multilevel logistic regression models were utilised. In 1993, 1998, 2003, 2008, and 2013 the prevalence of overweight children was 5.3%, 4.9%, 10.0%, 11% and 11.6%, respectively. The factors that were independently associated with overweight/obesity were as follows: living in single-parent households (adjusted odds ratio (aOR) = 2.27, 95%CI = 1.21-4.26), compared to living in dual-parent households; having an obese mother (aOR = 4.25, 95%CI = 1.73-10.44), overweight mother (aOR = 3.15, 95%CI = 1.29-7.69), and a normal-weight mother (aOR = 2.70, 95%CI = 1.11-6.59) compared to having an underweight mother; being aged between 13-24 months (aOR = 1.72, 95%CI = 1.30 to 2.27), compared to being aged 0-12 months; male gender (aOR = 1.30, 95%CI = 1.11 to 1.53); being stunted (aOR = 2.18, 95%CI = 1.74 to 2.73); high birth weight (aOR = 1.55, 95%CI = 1.08 to 2.23) compared to low birth weight. In addition, overweight was higher in children of mothers who had completed primary school (aOR = 1.21, 95%CI = 1.01 to 1.59) than children of mothers who had not completed primary school. These findings reveal that, over the years, there has been a substantial increase in obesity/overweight among children which demonstrates the importance of evaluating the overweight indicators at the maternal/household level