Random forests resource allocation for 5G systems: Performance and robustness study

Next generation cellular networks are expected to improve aggregate multi-user sum rates by a thousand-fold, implying the deployment of cloud radio access networks (CRANs) that consist of a dense set of radio heads. Such a densification of the network inevitably results in high interference coordination complexity and is associated with significant channel state information (CSI) acquisition overhead. The main hypothesis behind this study is that both the coordinated resource allocation complexity and the signaling overhead can be significantly reduced by exploiting explicit knowledge about a terminal's position to make resource allocation predictions. More specifically, we present a design of a learning-based resource allocation scheme for 5G systems that uses Random Forests as multi-class classifier to predict the modulation and coding scheme of a terminal at any given position served by the CRAN. Through performance evaluations it is shown that the signaling overhead is significantly reduced while the learning-based resource allocation scheme can achieve a comparable spectral efficiency to CSI-based schemes. We demonstrate the robustness of the proposed scheme for a varying accuracy of users' positions, showing that even for quite large variations the learning-based approach can still exhibit good performance.