International Journal of Distributed Systems and Technologies, 2(2), 1-19, April-June 2011 1 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Intelligent Ray Launching, Network Planning, Parallelization, Propagation Prediction, Simulation IntroductIon Propagation modeling serves as a fundamental input in the wireless network planning and optimization process. Especially, in order to determine the interferences for an indoor fem- the development of a Parallel ray Launching Algorithm for Wireless network Planning Zhihua Lai, University of Bedfordshire, UK Nik Bessis, University of Bedfordshire, UK Guillaume De La Roche, University of Bedfordshire, UK Pierre Kuonen, University of Applied Science of Western Switzerland, Switzerland Jie Zhang, University of Bedfordshire, UK Gordon Clapworthy, University of Bedfordshire, UK 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 in- vestigated, however, due to the running time constraint, these approaches are still not widely used. In previ- ous 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 suffcient physical resources. tocell base station with the outdoor macrocell, accurate coverage predictions have to be ob- tained via propagation modeling (Zhang & De La Roche, 2010). Planning and optimization of a wireless network usually requires simulation of hundreds of User Equipments (UE) and the path loss between these UEs and base stations DOI: 10.4018/jdst.2011040101