Advanced Ray-optical Wave Propagation Modellingfor Urban and Indoor Scenarios

Abstract

Ray-optical propagation models are often utilized for the prediction of the field strength (and delay spread) in mobile radio networks. However, the practical usage of these deterministic models is limited due to their high computational demands. A new method for the acceleration of ray-optical models is presented in this paper. It is based on a single preprocessing of the database in which the mutual visibility relations between the walls and the edges of the buildings are determined. The propagation model is implemented for urban and indoor scenarios and comparisons with measurements show the gain in computation efficiency as well as in achieved prediction accuracy. INTRODUCTION The performance of wireless communication systems depends in a fundamental way on the mobile radio channel. As a consequence predicting the propagation characteristics between two antennas still belongs to the most important tasks for the design and installation of cellular mobile communication systems [1]. According to the growing number of subscribers during the last years, the size of cells had to be reduced from radii in the order of tens of kilometres within rural and suburban environments (macrocells) down to a few hundreds of metres in urban scenarios (microcells) and even further down to some 10 m with indoor applications (picocells). With decreasing size of the cells the importance of wave propagation modelling within urban and indoor scenarios increases with regard to the extension of present and the deployment of future systems. This paper introduces a new approach for modelling wave propagation within these scenarios in an accurate way with minimized computational complexity. The Mobile Radio Channel The mobile radio channel is characterized by a multipath situation. The signal transmitted by the base station – if only the downlink is considered here – will travel along different paths to the receiving antenna of the mobile station. In many cases there is no direct line of sight and the only signals reaching the receiver have undergone reflections, scattering and diffractions at a number of different obstacles. Consequently the field strength in a radio cell shows small-scale fading. While deterministic ray-based propagation models, as described later, are able to compute the smallscale fading, planning tools for the prediction of field strength levels will generally provide only mean or median values as small-scale fading is adequately represented by Rayleighor Rice-distributions [2]. Data Bases for Buildings Data bases used with radio propagation models contain information on the kind of obstacles between the transmitter (base station) and the receiver (mobile station) and are a compulsory requirement for using the more sophisticated prediction tools. Fig. 1: Data bases describing urban (left) and indoor (right) environments, respectively While rural propagation models are generally based on terrain and morphological data in pixel format, urban data bases contain information on the location of buildings and are generally vector oriented. In the vector format, the shape of every building is defined by its corners and its height. All buildings are consequently represented by cylinders with a polygonal plan view (see Fig. 1). Indoor data bases are 3D and include all walls, doors, windows and possibly furniture. All elements inside the building are described in terms of plane elements. Every wall is e.g. represented by a plane and its extent and location is defined by its corners (see Fig. 1). For each wall individual material properties can be defined. WAVE PROPAGATION MODELS While other wireless communication networks, like e.g. directive radio links, operate under line-of-sight (LOS) conditions and can use a simple free space propagation model, mobile communication, as outlined above, is generally non-line-of-sight (NLOS) and requires more sophisticated approaches. Most of the widely employed methods for the prediction of field strength in different scenarios are based on empirical equations. Consequently these models offer short computation times but on the other hand less accuracy in comparison to deterministic approaches [3]. Deterministic Models Deterministic propagation models are generally based on ray optical techniques. Their common idea is to describe wave propagation by different rays that travel from the transmitting to the receiving antenna and are subject to reflection, scattering and diffraction at walls and edges of buildings and similar obstacles. The computations are performed with help of the universal theory of diffraction (UTD). The most time-consuming part of a field-prediction based on this method is finding all the relevant paths from transmitter to receiver. For this purpose either the ray tracing or the ray launching algorithm is used (as indicated in Fig. 2). While empirical models [4] assume straight propagation from transmitter to receiver, regardless of any obstacles such as buildings or walls, deterministic models consider the physical paths along which the transmitted waves propagate. As a consequence, deterministic models cope with effects such as wave guiding in street canyons, offer excellent accuracy and are able to provide additional parameters such as small-scale fading or delay spread. Their main disadvantage consists in their sometimes prohibitively large computation time. Ray Launching Ray Tracing

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Cite this paper

@inproceedings{Landstorfer2002AdvancedRW, title={Advanced Ray-optical Wave Propagation Modellingfor Urban and Indoor Scenarios}, author={Friedrich M. Landstorfer}, year={2002} }