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

TL;DR

This paper proposes a graph theory-based method for user grouping and downlink scheduling in FDD Massive MIMO systems, significantly improving sum-rate and fairness by optimizing user partitioning and scheduling.

Contribution

It introduces a novel similarity measure and clustering technique, along with a low complexity scheduling scheme, to enhance FDD Massive MIMO performance.

Findings

Outperforms existing methods in sum-rate

Achieves better throughput fairness

Demonstrated through computer simulations

Abstract

Massive MIMO is considered as one of the key enablers of the next generation 5G networks.With a high number of antennas at the BS, both spectral and energy efficiencies can be improved. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antenna. This does not create complications in Time Division Duplex (TDD) systems since the channel estimate of the uplink direction can be directly utilized for link adaptation in the downlink direction. However, this channel reciprocity is unfeasible for the Frequency Division Duplex (FDD) systems where different physical transmission channels are existent for the uplink and downlink. In the aim of reducing the amount of Channel State Information (CSI) feedback for FDD systems, the promising method of two stage beamforming transmission was introduced. The performance of this transmission scheme is however highly influenced by the users grouping and selection mechanisms. In this paper, we first introduce a new similarity measure coupled with a novel clustering technique to achieve the appropriate users partitioning. We also use graph theory to develop a low complexity groups scheduling scheme that outperforms currently existing methods in both sum-rate and throughput fairness. This performance gain is demonstrated through computer simulations.