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Machine Learning Assisted Cell Association for Two-Tier Communication Networks

With the widespread use of fifth generation (5G) mobile communication networks, a much faster increase is expected in the already intense data traffic. This increasing need for traffic will bring along some problems. Ultra dense heterogeneous networks are seen as a solution for 5G communication system requirements and are expected to form the basis of these networks. In this study, a new optimization problem is proposed to associate users with cells in ultra dense heterogeneous networks, and the problem is solved with the help of our own neural network. After the users were associated with the cells, the accuracy and speed performance of the proposed method were compared with brute force solution.