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Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shownto provide significant improvements in exploiting spatial diversity in wirelessnetworks and provide high throughput for all users while minimisinginterference among them. Hence, one of the major challenges for such HRSschemes is the necessity to know the optimal clustering of these users basedonly on their Channel State Information (CSI). This clustering problem is knownto be NP hard and, to deal with the unmanageable complexity of finding anoptimal solution, in this work a scalable and much lighter clustering mechanismbased on Neural Network (NN) is proposed. The accuracy and performance metricsshow that the NN is able to learn and cluster the users based on the noisychannel response and is able to achieve a rate comparable to other more complexclustering schemes from the literature.

User Clustering for Rate Splitting using Machine Learning