User scheduling in massive MIMO systems with a large number of devices

In this work, we consider a joint grouping and scheduling algorithm for fair user scheduling in massive multiple-input multiple-output (MIMO) systems that support Internet of Things (IoT) application scenarios. Our proposed method serves the users consecutively in groups, where the group sizes are derived from the optimal number of active users in the zero-forcing downlink channel. To select the best group members we adopt a version of the popular semi-orthogonal user selection (SUS) algorithm. Specifically, we identify the drawbacks of the original SUS algorithm when operating in a massive MIMO system and present a modified SUS strategy (SUS-M) for user selection. Simulation results demonstrate that our proposed method outperforms the conventional SUS strategy and provides improved fairness in terms of the individual user rates.