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An Inversion Propagation Model Using GA for Coverage Prediction of a Single Urban Cell in Wireless Network

In order to catch the iterated growth and evolution of the future mobile communications, this paper has proposed to study on a novel prediction model for radio propagation as well with its applications based on the radio propagation theory and the inverse theory. The prediction of radio propagation in a mobile network can be treated as an inverse problem. Instead of through the high-precision geometric modeling of wireless environments, this problem can be solved by an inversion of the measured data (under all priori constraints). So a complicated propagation prediction problem can be simplified to a system of large scale ill-condition equations, which can be solved by genetic algorithm appropriately. The effectiveness of the proposed method has been demonstrated using experiments under various radio environments in Guang Dong, China. It was shown that the prediction results were approximately consistent with independent checking samples. The advantages of the proposed strategies compared with existing approaches are well demonstrated.