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

Abstract

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.Indexing Terms: Ray-tracing based radio propagation prediction model, Radio wave prediction model on Network of Workstations (NOW), Data or control domain decomposition, Dynamic workload-balancing scheduling schemes.

The Horus location determination system

“…Given the location of the APs, information about the building construction, and a propagation model, an estimate of the radio map can be generated [28,29]. Automatic generation of the radio map has a number of advantages: (1) it reduces the time and effort required to build the radio map, (2) it make it easier to incorporate changes in the environment, such as addition of new APs, and (3) it can be used to detect the effect of using different hardware.…”

Computational parallelization strategy applied in full 3D ray-tracing wireless channel modeling

“…In [5], the strategy of parallelization 0-7803-9342-2105/$20.00 @ 2005 IEEE 359 proposed is very complex and difficult to implement. This approach was applied in a 3D ray-tracing model witb some restrictions in the diffraction mechanism (Vertical Plane model) [2] and is very dependent on the SBR algorithm implementation.…”

“…Recently, some parallel computation strategies have been proposed in order to reduce the required compu tational time without affecting the prediction accuracy requirements [5], [8]. In [5], the strategy of parallelization proposed is very complex and difficult to implement.…”

“…In this context, ray-tracing based radio propagation prediction models have shown promise, mainly in modem radio wave propagation en vironments [1]- [6]. Although ray-tracing approaches are very useful in the design, analysis, and deployment of wireless networks, it has been recognized that these models are computationally very expensive and require a con siderable amount of processing time to attain reasonable accurate prediction results [2], [5].A number of approaches has been proposed to shorten the computation time for ray-tracing prediction models. In [2], the complexity of the building databases was reduced by simplifying footprints.…”

“…All these approaches have a common trade-off: they trade prediction accuracy for processing time. A natural way to overcome the above trade-off is to use the parallel and/or distributed computation techniques to speed up computations, while keeping the accuracy intact [5]. More specifically, the usage of a cluster of PC's (sometimes referred as a class of COW's -Cluster of Workstations) is particularly attractive as such computer system confi gurations are readily available at this time.…”