The statistical properties of the received signal are significantly dependent on the direction of arrival at the receiving antenna. Therefore, knowledge of the arrival angles is useful in simulation studies of wireless channels. This study presents a method for determining the arrival angles and their intensity in the azimuth and elevation planes. Besides, the impact of the propagation environment and radiation pattern of the transmitting antenna on the scattering intensity of the arrival angles is shown. This significantly distinguishes the proposed technique from the methods for determining signal direction that have been previously described in the literature. The procedure is based on a geometric description of the scattering areas, which are a set of half‐ellipsoids. Their number and size result from the parameters of the power delay spectrum or profile for modelled propagation environment. Simulation studies offer the possibility of adapting the statistical properties of the arrival angles to the type of propagation environment examined. The inclusion of the propagation phenomena in the azimuth and elevation planes provides a better representation of the empirical results compared with two‐dimensional modelling. This fact is shown by a comparison of statistical properties between the simulated directions and the measurement results for selected types of propagation environments.
A method to evaluate the statistical properties of the reception angle seen at the input receiver that considers the receiving antenna pattern is presented. In particular, the impact of the direction and beamwidth of the antenna pattern on distribution of the reception angle is shown on the basis of 3D simulation studies. The obtained results show significant differences between distributions of angle of arrival and angle of reception. This means that the presented new method allows assessing the impact of the receiving antenna pattern on the correlation and spectral characteristics at the receiver input in simulation studies of wireless channel. The use of this method also provides an opportunity for analysis of a co-existence between small cells and wireless backhaul, what is currently a significant problem in designing 5G networks.Index Terms-Angle of arrival, angle of reception, angle spread, antenna radiation pattern, azimuthal and elevational planes, channel models, channel modeling, directional receiving antenna, geometric channel models, half power beamwidth.
The paper presents an estimation of the reception angle distribution based on temporal characteristics such as the power delay spectrum (PDS) or power delay profile (PDP). Here, we focus on such wireless environment, where the propagation phenomenon predominates in azimuth plane. As a basis to determine probability density function (PDF) of the angle of arrival (AOA), a geometrical channel model (GCM) in form of the multielliptical model for delayed scattering components and the von Mises’ PDF for local scattering components are used. Therefore, this estimator is called the distribution based on multielliptical model (DBMM). The parameters of GCM are defined on the basis of the PDS or PDP and the relative position of the transmitter and the receiver. In contrast to the previously known statistical models, DBMM ensures the estimation PDF of AOA by using the temporal characteristics of the channel for differing propagation conditions. Based on the results of measurements taken from the literature, DBMM verification, assessment of accuracy, and comparison with other models are shown. The results of comparison show that DBMM is the only model that provides the smallest least-squares error for different environments.
In the proposed chapter, the authors present a geometric-statistical propagation model that defines three groups of received signal components, i.e., direct path, delayed scattering, and local scattering components. The multi-elliptical propagation model, which represents the geometry of scatterer locations, is the basis for determining the delayed components. For the generation of the local components, a statistical distribution is used. The basis for this model is a power angular spectrum (PAS) of the received signal, which is closely related to a type of propagation environment and transmitter-receiver spatial positions. Therefore, we have an opportunity to evaluate the influence of the environment type and an object motion direction on the basic characteristics such as envelope distribution, PAS, autocorrelation function, and spectral power density. The multi-elliptical model considers the propagation phenomena occurring in the azimuth plane. In the chapter, we will also show the 3D extension of modeling effects of propagation phenomena.
The main drawback of channel models presented in the literature is the lack of physically justified integration of all basic phenomena such as fluctuations, channel dispersion, and selective fading that occur in the actual radio channels. Based on physical premises, presented in this paper, the developed channel model reproduces all basic phenomena that affect the temporal, correlational-spectral, and spatial characteristics of the modelled radio channels. This effect is achieved by the structure of the model, which includes both the geometric channel model and statistical models of the received signal parameters. The geometry used in the model is based on the Parsons-Bajwa multi-elliptical model and the relationship, which describes the Doppler frequency as a function of the spatial position of object. Therefore, this model is called the Doppler multi-elliptical channel model (DMCM). The source of input data that define the geometric and statistical parameters of the model is the power delay profile or the power delay spectrum. This makes sure that DMCM characteristics depend on the properties of the modelled propagation environment. A comparative analysis of the simulation results and the measurements that DMCM can correctly capture the actual transmission properties of real channels. Keywords Wireless communications Á Mobile channel Á Geometric channel model (GCM) Á Multipath channel Á Rayleigh fading Á Scattering Á Doppler multi-elliptical channel model (DMCM)
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