LAUSR

The use of low-resolution data converters is regarded as a cost-effective solution to reduce the hardware complexity and power consumption of massive multiuser multiple-input and multiple-output (MIMO) systems. In this study, we focus on the problem of joint antenna selection and user scheduling (JASUS) in massive multiuser MIMO uplink systems, in which the base station (BS) is equipped with low-resolution analog-to-digital converters. We aim to simultaneously obtain the optimal BS antenna and user sets that maximize the system sum rate. However, finding the optimal solution to the JASUS problem requires an exhaustive search of all possible combinations of BS antennas and users, and this search incurs a combinatorial complexity that scales exponentially with the number of BS antennas and users. We address this problem by proposing a novel algorithm developed from the cross-entropy optimization (CEO) framework. The simulations produce promising results for the proposed CEO-based JASUS algorithm, which achieves a higher sum rate and a lower symbol error rate compared with other test algorithms.