Adaptive user grouping algorithm for the downlink massive MIMO systems

Channel correlation between different users can significantly deteriorate the energy and spectral efficiency of massive MIMO systems. As such, it is tempting to separate the highly correlated users in order to reduce intra-cell interference to improve the overall system performance. In this respect, and unlike existing work, we propose an adaptive user grouping algorithm for the downlink of multiuser massive MIMO systems where users are grouped based on the values of their correlation coefficients. The proposed algorithm is adaptive in the sense that the number of neither the groups nor the users of each group is fixed. Furthermore, the grouping process relies on finding the correlation coefficients that are larger than a certain threshold value and isolating their corresponding users in separate groups which are served in different scheduled time. The threshold value is variable and can be adjusted to achieve the best performance compared to the non grouping scenario. The results reveal that the proposed system can considerably enhance the sum rate performance relative to the non-grouping case for the same bandwidth requirements.