# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
###### Abstract
In [13], the spectral efficiency of massive MIMO systems operating in Rician fading channels is evaluated through simulations. In this work, it is shown that reducing the service area and increasing the number of receiving antennas improves the system performance. For the same type of fading channels, in [14] it is shown that when the Rician \(K\) factor increases, the sum-rate capacity of the uplink of MIMO systems improves. It was also observed that when the number of antennas in the BS is increased, the undesirable effects of imperfect channel estimation are reduced. Further, in [15], the uplink of MIMO systems using MRC detectors in Rician fading channels is studied. Specifically, it was determined that as the Rician \(K\) factor increases, the adverse effects produced by imperfect channel estimation at the receiver are reduced. In [16], the performance of massive MIMO systems in correlated Rician fading is evaluated. The authors analyze spectral efficiency in systems composed of multiple cells considering various channel estimators. In [17], the performance of MIMO systems is analyzed considering different types of fading. Specifically, Rayleigh, Rice, Nakagami and Weibull statistical distributions are employed in this work for fading modeling. Through numerical simulations, the BER and the capacity of the system are analyzed in scenarios with and without perfect channel estimation. Multiuser detectors play a fundamental role in the proper operation of MIMO systems. In the literature, different detectors have been proposed that are characterized by their trade-off between performance and implementation complexity. In [18], it is indicated that the use of linear detectors in MIMO receivers is an interesting alternative from the complexity point of view. This work focuses on the study of linear detectors for the uplink of MIMO systems operating in correlated Rician fading channels. In [19], a multicarrier MIMO system using linear detectors is evaluated in terms of the mean BER. Thus, approximate expressions are obtained to evaluate this performance indicator. The results show that the minimum-mean-square-error (MMSE) detector has a better performance than the zero-forcing (ZF) detector, but the MMSE implementation requires knowledge of the instantaneous noise power. Several investigations aim to optimize the performance of MU-MIMO via scheduling algorithms, which select a group of users or assign radio resources to the users based on different criteria [20; 21]. For instance, in [22], a channel allocation strategy for MU-MIMO systems using precoding techniques is proposed for broadcasting applications. In [23], a Rician fading channel is considered where the number of users is greater than the number of pilot sequences. In this context, a component identification algorithm is proposed. In [24], it is established that channel allocation algorithms are necessary for achieving good performance in MIMO and massive MIMO systems. In addition, a channel grouping and allocation algorithm based on LOS conditions is proposed. In [25], an algorithm for selecting users experiencing good channel conditions is presented, but users at the edges of cells are often ignored with this proposal. In [26], a scheduling scheme based on the individual gain of each user's channel is proposed. Its performance is evaluated in terms of the OP. In all the aforementioned works, perfect channel estimation at the receivers is considered, which is not fulfilled in real scenarios [8]. Finally, in [27], the authors present a state-of-the-art analysis related to channel allocation (scheduling) algorithms for the downlink of massive MIMO systems. In particular, the authors present schemes based on capacity, Frobenius norm, conditional entropy, and volume, along with diagonalization pre-processing for eliminating interference between users.