Artificial Neural Network model for the determination of GSM Rxlevel from atmospheric parameters

Accurate received signal level (Rxlevel) values are useful for mobile telecommunication network planning. Rxlevel is affected by the dynamics of the atmosphere through which it propagates. Adequate knowledge of the prevailing atmospheric conditions in an environment is essential for proper network planning. However most of the existing GSM received signal determination model are function of distance between point of signal reception and transmitting site thus necessitating the development of a model that involve the use of atmospheric parameters in the determination of received GSM signal level. In this paper, a three stage approach was used in the development of the model using some atmospheric parameters such as atmospheric temperature, relative humidity and dew point. The selected and easily measurable atmospheric parameters were used as input parameters in developing two new models for computing the Rxlevel of GSM signal using a three-step approach. Data acquisition and pre-processing serves as the first stage and formulation of ANN design and the development of parametric model for the Rxlevel using ANN synaptic weights form the second stage of the proposed approach. The third stage involves the use of ANN weight and bias values, and network architecture in the development of the model equation. In evaluating the performance of the proposed models, network parameters were varied and the results obtained using mean squared error (MSE) as performance measure showed the developed model with 33 neurons in the hidden layer and tansig activation, function in both the hidden and output layers as the optimal model with least MSE value of 0.056. Thus showing that the developed model has an acceptable accuracy value as demonstrated from comparison of results with actual measured values.