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Propagation models are keys components of coverage planning. With the deployment of 4G network worldwide, operators need to plan the coverage of their network efficiently, in order to minimize cost and improve the quality of service.In this paper, the standard model K factors is taken into account to develop a method for tuning propagation models based on particle swarm optimization algorithm. The data are collected on the existing CDMA2000 1X-EVDO rev B network in the town of Yaoundé, capital of Cameroon. The root mean squared error (RMSE) between actual measurements and radio data obtained from the prediction model developed is used to test and validate the technique. The values of the RMSE obtained by the new model and those obtained by the standard model of OKUMURA HATA in urban area are also compared. Through the comparison of RMSE from optimized model and OKUMURA HATA, it can be concluded that the new model developed using particle swarm optimization performs better than the OKUMURA HATA model and is more accurate. The new model is also more representative of the local environment and also similar to the optimized model obtained when using linear regression method. This method can be applied anywhere to optimize existing propagation model.

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard K factors model and then uses the Ion motion optimization (IMO) algorithm to set up a propagation model adapted to the physical environment of each of the Cameroonian cities of Yaoundé and Bertoua for different frequencies and technologies. Drive tests were made on the CDMA network in the city of Yaoundé on one hand and on an LTE TDD network in the city of Bertoua on the other hand. IMO is used as the optimization algorithm to deduct a propagation model which fits the environment of the two considered towns. The calculation of the root-mean-square error (RMSE) between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura-Hata and K factors standard models, allowed us to conclude that the new model obtained in each of these two cities is better and more representative of our local environment than the Okumura-Hata currently implemented. The implementation shows that IMO can perform well and solve this kind of optimization problem; the newly obtained models can be used for radio planning in the cities of Yaounde and Bertoua in Cameroon.

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain; resulting in reduced service disruption time and improved the network availability which is a key network performance index.

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