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The Development of a Parallel Ray Launching Algorithm for Wireless Network Planning

The Development of a Parallel Ray Launching Algorithm for Wireless Network Planning

Zhihua Lai, Nik Bessis, Guillaume De La Roche, Pierre Kuonen, Jie Zhang, Gordon J. Clapworthy
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 19
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781613506622|DOI: 10.4018/jdst.2011040101
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MLA

Lai, Zhihua, et al. "The Development of a Parallel Ray Launching Algorithm for Wireless Network Planning." IJDST vol.2, no.2 2011: pp.1-19. http://doi.org/10.4018/jdst.2011040101

APA

Lai, Z., Bessis, N., De La Roche, G., Kuonen, P., Zhang, J., & Clapworthy, G. J. (2011). The Development of a Parallel Ray Launching Algorithm for Wireless Network Planning. International Journal of Distributed Systems and Technologies (IJDST), 2(2), 1-19. http://doi.org/10.4018/jdst.2011040101

Chicago

Lai, Zhihua, et al. "The Development of a Parallel Ray Launching Algorithm for Wireless Network Planning," International Journal of Distributed Systems and Technologies (IJDST) 2, no.2: 1-19. http://doi.org/10.4018/jdst.2011040101

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Abstract

Propagation modeling has attracted much interest because it plays an important role in wireless network planning and optimization. Deterministic approaches such as ray tracing and ray launching have been investigated, however, due to the running time constraint, these approaches are still not widely used. In previous work, an intelligent ray launching algorithm, namely IRLA, has been proposed. The IRLA has proven to be a fast and accurate algorithm and adapts to wireless network planning well. This article focuses on the development of a parallel ray launching algorithm based on the IRLA. Simulations are implemented, and evaluated performance shows that the parallelization greatly shortens the running time. The COST231 Munich scenario is adopted to verify algorithm behavior in real world environments, and observed results show a 5 times increased speedup upon a 16-processor cluster. In addition, the parallelization algorithm can be easily extended to larger scenarios with sufficient physical resources.

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