TY - JOUR
T1 - The development of a parallel ray launching algorithm for wireless network planning
AU - Lai, Zhihua
AU - Bessis, Nik
AU - De La Roche, Guillaume
AU - Kuonen, Pierre
AU - Zhang, Jie
AU - Clapworthy, Gordon
N1 - Funding Information:
This work was supported by the EU-FP7 iPLAN and FP6 GAWIND under grant number MTKD-CT-2006-042783 (“Marie Curie Fellowship for Transfer of Knowledge”).
PY - 2011/4/1
Y1 - 2011/4/1
N2 - 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.
AB - 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.
KW - Intelligent Ray Launching
KW - Network Planning
KW - Parallelization
KW - Propagation Prediction
KW - Simulation
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U2 - 10.4018/jdst.2011040101
DO - 10.4018/jdst.2011040101
M3 - Article (journal)
AN - SCOPUS:84880524614
SN - 1947-3532
VL - 2
SP - 1
EP - 19
JO - International Journal of Distributed Systems and Technologies
JF - International Journal of Distributed Systems and Technologies
IS - 2
ER -