# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications  
[PERSON]  
1 Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, 40126 Bologna, Italy; [EMAIL_ADDRESS]
2 Center for Industrial Research on ICT (CIRI-ICT), University of Bologna, 40126 Bologna, Italy 2  
[PERSON]  
3 ZaiNar Inc., Redwood City, CA 94063, USA; [EMAIL_ADDRESS]  
[PERSON]  
4 Polaris Wireless Inc., Mountain View, CA 94043, USA; [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (J.J.Z)  
[PERSON]  
4 Polaris Wireless Inc., Mountain View, CA 94043, USA; [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (J.J.Z)  
[PERSON]  
1 Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, 40126 Bologna, Italy; [EMAIL_ADDRESS]
2 Center for Industrial Research on ICT (CIRI-ICT), University of Bologna, 40126 Bologna, Italy 2  
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spatial description of propagation, the increased availability of high-performance computation platforms and of digitized urban maps, and the increasing use of map-based design approaches, to name the most important ones. Despite this trend, ray-based prediction for large-scale RF coverage problems in macro-cellular, drone-based, and satellite systems, is barely addressed in scientific studies and industrial applications due to the overwhelming computation time required on ordinary computers, especially if multiple diffraction and diffuse scattering phenomena are taken into account. To the authors' knowledge, only a few literature studies addressed large-scale, deterministic RF coverage prediction for realistic cellular network design problems [5; 6]. Therefore, methods to improve computation time efficiency of ray-based models have been proposed over the years, including the use of 2D approaches [7; 8], quasi-3D approaches [9], environment discretization and pre-processing [10], reduction of the urban database and of the number of rays to be traced [11], or cloud computing techniques [12]. Conceived for computer graphics, Graphic Processing Units (GPUs) are now used for a variety of computationally intensive applications such as genomic computation [13], data processing [14], and biomedical applications [15]. Recently, GPU computation has also been proposed by several authors to parallelize ray-based models and therefore greatly increase their efficiency [16; 17; 18; 19; 20; 21]. The maximum speed-up is obtained using RL approaches, intrinsically more efficient and more suitable than RT to parallelization. GPU-based approaches have been applied with success to propagation prediction in vehicular networks [22], drone-based applications [23; 24], and indoor environments represented through point-cloud techniques [25; 26]. The Discrete, Environment Driven Ray Launching algorithm (DED-RL), first proposed in [20] and fully described in [21] is a particularly interesting GPU-based algorithm where multiple techniques, namely, environment database discretization and pre-processing, environment-driven ray-launching, and GPU parallelization, work in synergy in order to reach an unprecedented level of computational efficiency. Thanks to the DED-RL approach, problems of previously unapproachable sizes can be solved within minutes on a standard PC equipped with a high-performance GPU [21]. In order to fully assess its capabilities with respect to more traditional approaches, DED-RL is validated in the present work for the first time against a large set of RF coverage measurements encompassing over 500 base station sites, thousands of receivers per site with a link distance ranging from a few meters to some kilometers, within several major urban areas including the very challenging, high-rise core of the city of San Francisco.