Low-Complexity Downlink User Selection for Massive MIMO Systems

TL;DR

This paper introduces two low-complexity user selection schemes for massive MIMO downlink systems that optimize sum rate without requiring real-time small-scale channel information, improving efficiency and fairness.

Contribution

The paper develops two novel user selection schemes, $K^*$-RUS and $K^*$-LUS, which maximize sum rate while maintaining low computational complexity and independence from small-scale fading information.

Findings

Both schemes outperform conventional random user selection in sum rate.

The schemes achieve similar fairness levels as traditional methods.

They require significantly less computational effort.

Abstract

In this paper we propose a pair of low-complexity user selection schemes with zero-forcing precoding for multiuser massive MIMO downlink systems, in which the base station is equipped with a large-scale antenna array. First, we derive approximations of the ergodic sum rates of the systems invoking the conventional random user selection (RUS) and the location-dependant user selection (LUS). Then, the optimal number of simultaneously served user equipments (UEs), $K^*$, is investigated to maximize the sum rate approximations. Upon exploiting $K^*$, we develop two user selection schemes, namely $K^*$-RUS and $K^*$-LUS, where $K^*$ UEs are selected either randomly or based on their locations. Both of the proposed schemes are independent of the instantaneous channel state information of small-scale fading, therefore enjoying the same extremely-low computational complexity as that of the conventional RUS scheme. Moreover, both of our proposed schemes achieve significant sum rate improvement over the conventional RUS. In addition, it is worth noting that like the conventional RUS, the $K^*$-RUS achieves good fairness among UEs.