Overlapping User Grouping in IoT Oriented Massive MIMO Systems

This paper considers capacity and quality of service improvement in Internet of Things (IoT) oriented massive MIMO systems through overlapping user grouping. In massive MIMO systems, user selection and grouping are generally used to reduce multiuser interference. In existing approaches, users with less favorable channel conditions are generally dropped for capacity optimization. As a result, some users would never be served by the system in IoT networks. Moreover, user subgroups are generally non-overlapping, leading to unnecessary resource waste. Motivated by these observations, in this paper, we propose two new user grouping approaches. First, we propose a new user grouping method based on greedy algorithm by allowing overlapping between the selected subgroups. Second, we introduce a new channel similarity measure, and develop another overlapping user grouping method by exploiting the spectral clustering method in machine learning. It is observed that the proposed approaches can increase the system capacity through subgroup overlapping, and can ensure that each user will be served in at least one subgroup.