Machine Learning Uplink Power Control in Single Input Multiple Output Cell-free Networks

Abstract: This thesis considers the uplink of cell-free single input multiple output systems, in which the access points employ matched-filter reception. In this setting, our objectiveis to develop a scalable uplink power control scheme that relies only on large-scale channel gain estimates and is robust to changes in the environment. Specifically, we formulate the problem as max-min and max-product signal-to-interference ratio optimization tasks, which can be solved by geometric programming. Next, we study the performance of supervised and unsupervised learning approaches employing a feed-forward neural network. We find that both approaches perform close to the optimum achieved by geometric programming, while the unsupervised scheme avoids the pre-computation of training data that supervised learning would necessitate for every system or environment modification.