Machine Learning for Forecasting Signal Strength in Mobile Networks

In this thesis we forecast the future signal strength of base stations in mobile networks. Better forecasts might improve handover of mobile phones between base stations, thus improving overall user experience. Future values are forecast using a series of past sig- nal strength measurements. We use vector autoregression (VAR), a multilayer perceptron (MLP), and a gated recurrent unit (GRU) network. Hyperparameters, including the set of lags, of these models are optimised using Bayesian optimisation (BO) with Gaussian pro- cess (GP) priors. In addition to BO of the VAR model, we optimise the set of lags in it using a standard bottom-up and top-down heuristic. Both approaches result in similar predictive mean squared error (MSE) for the VAR model, but BO requires fewer model estimations. The GRU model provides the best predictive performance out of the three models. How- ever, none of the models (VAR, MLP, or GRU) achieves the accuracy required for practical applicability of the results. Therefore, we suggest adding more information to the model or reformulating the problem