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Massive MIMO is considered as one of the key enablers of the next generation5G networks.With a high number of antennas at the BS, both spectral and energyefficiencies can be improved. Unfortunately, the downlink channel estimationoverhead scales linearly with the number of antenna. This does not createcomplications in Time Division Duplex (TDD) systems since the channel estimateof the uplink direction can be directly utilized for link adaptation in thedownlink direction. However, this channel reciprocity is unfeasible for theFrequency Division Duplex (FDD) systems where different physical transmissionchannels are existent for the uplink and downlink. In the aim of reducing theamount of Channel State Information (CSI) feedback for FDD systems, thepromising method of two stage beamforming transmission was introduced. Theperformance of this transmission scheme is however highly influenced by theusers grouping and selection mechanisms. In this paper, we first introduce anew similarity measure coupled with a novel clustering technique to achieve theappropriate users partitioning. We also use graph theory to develop a lowcomplexity groups scheduling scheme that outperforms currently existing methodsin both sum-rate and throughput fairness. This performance gain is demonstratedthrough computer simulations.

Graph Theory Based Approach to Users Grouping and Downlink Scheduling in FDD Massive MIMO