Abstract
Algorithms used in the optimization of wireless deployments have computational time as one of their main drawbacks. This computational time is very dependent on the test points established to characterize a deployment scenario. The approach commonly used to define sample points is based on a regular grid, which leads to a remarkable number of sample points if accuracy is required. In this paper, a novel scenario-dependent sampling scheme based on mathematical morphology is presented. Its performance is evaluated and compared to regular sampling, outperforming typical schemes reducing computational time while maintaining very accurate results.
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