Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

• Authors: Zhongqiang Chen;Alex Delis;Henry L. Bertoni
• Venue: Journal of Parallel and Distributed Computing
• Year: 2004

Ray-tracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensive even for moderately complex geographic environments. In this paper, we propose a computational framework that functions on a network of workstations (NOW) and helps speed up the lengthy prediction process. In ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different ray-paths require different processing times. To address these widely varying needs, we propose a combination of the phase-parallel and manager/workers paradigms as the underpinning framework. The phase-parallel component is used to coordinate different computation stages, while the manager/workers paradigm is used to balance workloads among nodes within each stage. The original computation is partitioned into multiple small tasks based on either raypath-level or source-point-level granularity. Dynamic load-balancing scheduling schemes are employed to allocate the resulting tasks to the workers. We also address issues regarding main memory consumption, intermediate data assembly, and final prediction generation. We implement our proposed computational model on a NOW configuration by using the message passing interface (MPI) standard. Our experiments with real and synthetic building and terrain databases show that, when no constraint is imposed on the main memory consumption, the proposed prediction model performs very well and achieves nearly linear speedups under various workload. When main memory consumption is a concern, our model still delivers very promising performance rates provided that the complexity of the involved computation is high, so that the extra computation and communication overhead introduced by the proposed model do not dominate the original computation. The accuracy of prediction results and the achievable speedup rates can be significantly improved when 3D building and terrain databases are used and/or diffuse scattering effect is taken into account.