Self-Organizing Networks (SON) and a number of SO functions (SFs) have been proposed, e.g. in theLTE standard. Since SFs operate on the same network, adjusting the same set of parameters, conflicts arise. Mechanisms are thus required to resolve or minimize these conflicts. We propose Space-Time scheduling procedures that allow for separating the execution of SFs at different space and time points so as to minimize negative cross effects among the SFs.
Using two Q-learning based SFs, our results show that the combined scheduling in space and time ensures that SFs learn optimal behaviors that are only due to their own actions and not the peers’ actions. In doing so, they maintain good performance even within the shared environment.