Radio network planning with neural networks

Abstract

The increasing number of participants in modern mobile radio networks, especially with the prospect of the new CDMA systems, necessitates a more and more detailed and efficient radio network planning. The basis of network planning is always the prediction of the quality of transmission between the transmitter and the participant. In order to find the optimal location of the transmitters in a tolerable time, an automatic positioning of base stations in an urban area needs an accurate and fast propagation model. Presently, there is no satisfying combination of an accurate and fast propagation model and an algorithm which perform the positioning of base stations in large urban areas. A fast optimization algorithm for CDMA networks based on a fast and accurate propagation model is presented. For both, the coverage prediction and the optimization process neural networks are used. For the prediction of the coverage, which has to be optimized, a sophisticated backpropagation network is used whereas for the optimization process a self organizing map is applied. First results of the planning and optimization of an urban radio network based on these new algorithms are presented.