Description
Optimizing cellular networks has been a very difficult task for a long time. In these networks multiple problematic issues appear and the high number of parameters and variables to optimize makes it a difficult problem even for radio experts. Here an optimizer for a cellular network is presented, the Uplink Interference Optimizer. Specifically, the uplink interference problem (degradation of the signal transmitted from a user terminal to a base station) will be solved by constructing a model that predicts a variable that reflects the interference level (SINR) and then optimizing the parameters of the cellular network, based on the model that has been built, to reduce it. That way, we achieve the optimization in a single step, improving previous solutions based on RL that must iterate over the real cellular network for several weeks. The simulator model will be based on Graph Neural Networks (constructed with PyTorch and PyTorch Geometric), allowing us to consider the neighborhood of a cell to make a prediction, which enhances the prediction accuracy over all older models. Then, any algorithm could be run to improve the parameters based on the simulator model we have constructed.