ישראל מירסקי

Accurate assessment of the wireless coverage of a station is considered a key feature in 5G networks. Determining the reception coverage of transmitters becomes a complicated problem when there are interfering transmitters, and it becomes increasingly more complicated when the transmission powers of those transmitters are not uniform. In this paper, we compare different Machine Learning techniques that can be used to predict the wireless coverage maps. We consider the following Machine Learning methods: (1) Radial Basis Network; a type of Artificial Neural Network which typically uses Gaussian kernels, (2) an Artificial Neural Network which uses a sigmoid function as an activator,(3) A Multi-Layer Perceptron with two hidden layers, and (4) the K-Nearest-Neighbors technique. We show how it is possible to train the Neural Networks to generate coverage maps based on samples and we check the accuracy level of the learning process on a test set, using these four different learning techniques. The conclusion of our experiments is that if the sample points are randomly located, the Radial Basis Network and the Multi-Layer Perceptron perform better than the other methods. Thus, these models can be considered promising candidates for learning coverage maps, and can be used for efficient spectrum management within the framework of 5G cellular networks.