The explosive daily dependence on wireless communication services necessitates the research to establish ultrawideband communication systems with ultrahigh bit rate transmission capabilities. The advent of the fifth-generation (5G) microwave link transmitting at millimeter-wave (mm-wave) frequency band is a promising technology to accommodate the escalating demand for wireless services. In this frequency band, however, the behavior of the transmission channel and its climatic properties are a major concern. This is of particular importance in tropical regions where the climate is mainly rainy with large raindrop size and high rainfall rate that may interact destructively with the propagating signal and cause total attenuation for the signal. International Telecommunication Union (ITU) introduced a global rain attenuation model to characterize the effect of rain on the propagating signal at a wideband of frequencies. The validity of this model in tropical regions is still an open question for research. In this paper, real measurements are conducted at Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia, to investigate the impact of rain on the propagation of mm-waves at 26 GHz over the microwave 5G radio link system. Rainfall rate and rain attenuation data sets are collected for one year at one sample per min sampling rate. Both data sets are used to estimate signal propagation conditions in comparison to the ITU model prediction. From the presented results, it is found that at 0.01% percentage of time and rainfall rate of about 120 mm/hr, the propagated signal would experience 26.2 dB losses per kilometer traveled. In addition, there is a significant deviation between the empirical estimation of the worst month parameters and the ITU worst month parameter prediction. Similarly, rainfall rate and rain attenuation estimated through the ITU model imposes a large deviation as compared with the measurements. Furthermore, more accurate empirical worst month parameters are proposed that yielded more accurate estimation of the worst month rainfall and rain attenuation predictions in comparison to the ITU model predictions. Trans Emerging Tel Tech. 2019;30:e3697. wileyonlinelibrary.com/journal/ett
This paper proposes a new propagation model based on the most widely used Hata model. The proposed model is developed by extrapolating Hata model to be suitable for microcells. The main equation of Hata urban model is modified by substituting the suburban correction factor with a terrain roughness parameter. This parameter uses a quadratic regression estimator of the standard deviation, σ, of the terrain irregularities along the measuring path, in west of Amman, Jordan. It is shown that RMSE between the predicted and measured data for the new proposed model, is improved by up to 3 dB compared to Hata suburban model in most areas under study. Furthermore, the improvement in RMSE increases as σ increases. These results clarify the robustness of the proposed model.
Green technology is a new term which is used to describe the energy efficient technologies. The main aim of the energy efficient technology is to reduce the total energy consumption while maintaining the functionality and quality of the respective technology. In the context of mobile communications technology, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in dense areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the Long Term Evolution (LTE) mobile network. A real scenario from operators in Kuala Lumpur, Malaysia is used for the purpose of the investigation. Pathloss estimation is based on ERICSSON 999 method and the region of interest is represented as Digital Terrain Map (DTM). The planned base station locations, transmission power and heights are provided by the operator. Genetic Algorithm is developed to estimate the base station parameters for more energy efficient LTE network deployment. Results show that a remarkable energy saving of about 26% of the operator transmission power could be achieved by selecting the appropriate base station parameters in planning stage while maintaining the QoS of the network.
Object shape detection and localization techniques that utilize snake deformable models are one of the most promising image detection techniques. The binary edge maps, derived from the original image, are basically the class acted upon by the snake to extract the desired features. As a result, high and low energy content pixels are obtained. The high energy pixels are the pixels that reflect the object borders of a given image. This paper addresses a new external force that is calculated from the energy diffusion of high content pixels and is used to balance the internal forces of the snake. The proposed scheme showed better results in terms of computation speed and capture range than standard snake models. On the basis of the concavity convergence, analogous results are achieved in the proposed scheme compared with standard models.Index Terms-Algorithms, computer vision, deformable models, feature extraction, image edge detection.
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