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Next generation cellular networks will have to leverage large celldensifications to accomplish the ambitious goals for aggregate multi-user sumrates, for which CRAN architecture is a favored network design. This shifts theattention back to applicable resource allocation (RA), which need to beapplicable for very short radio frames, large and dense sets of radio heads,and large user populations in the coordination area. So far, mainly CSI-basedRA schemes have been proposed for this task. However, they have considerablecomplexity and also incur a significant CSI acquisition overhead on the system.In this paper, we study an alternative approach which promises lower complexitywith also a lower overhead. We propose to base the RA in multi-antenna CRANsystems on the position information of user terminals only. We use RandomForests as supervised machine learning approach to determine the multi-userRAs. This likely leads to lower overhead costs, as the acquisition of positioninformation requires less radio resources in comparison to the acquisition ofinstantaneous CSI. The results show the following findings: I) In general,learning-based RA schemes can achieve comparable spectral efficiency toCSI-based scheme; II) If taking the system overhead into account,learning-based RA scheme utilizing position information outperform legacyCSI-based scheme by up to 100%; III) Despite their dependency on the trainingdata, Random Forests based RA scheme is robust against position inaccuraciesand changes in the propagation scenario; IV) The most important factorinfluencing the performance of learning-based RA scheme is the antennaorientation, for which we present three approaches that restore most of theoriginal performance results. To the best of our knowledge, these insights arenew and indicate a novel as well as promising approach to master the complexityin future cellular networks.

Random Forest Resource Allocation for 5G Systems: Performance and Robustness Study