# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
###### Abstract  
For the receiver of multiple-input multiple-output (MIMO) systems, linear detectors are an interesting option due to their good performance and low complexity. Nevertheless, MIMO systems lose diversity in exchange for eliminating interference when linear detectors are used. Aiming to maintain the system diversity while mitigating interference between users, this work proposes a scheduling scheme for the uplink of multiuser MIMO (MU-MIMO) systems that employ \(A\) antennas and the zero-forcing (ZF) detector at the receiver in the base station (BS). The channel model includes Rician fading and additive white Gaussian noise (AWGN) in an imperfect channel estimation scenario. The proposed scheme selects \(U\) users from a group of \(U_{t}\) users to transmit simultaneously, so that the signal-to-noise ratio (SNR) is maximized. For this, an exact expression to evaluate the SNR of the users is obtained. With this result, the scheduling strategy is proposed. Results show that as \(U_{t}\) increases, the outage probability (OP), and the bit error rate (BER) decrease as the system diversity increases, even when the system is completely loaded, i.e., when \(U=A\). Moreover, it is shown that the scheduling scheme counteracts the imperfect channel estimation effects as \(U_{t}\) increases. Finally, the proposed scheme is tested in presence of an external eavesdropper trying to decode the information sent by the users. The results show that the presented proposal allows for a reduction of the secrecy-outage-probability (SOP) as \(U_{t}\) increases. MU-MIMO; zero-forcing; scheduling; imperfect channel estimation; [PERSON] fading 20222220222022220  
interest in the literature [4; 5]. In [6], the bit error rate (BER) of systems with \(N\) receiving antennas (diversity branches) is evaluated in non-ideal channel estimation scenarios, where an estimator structure based on maximum-likelihood (ML) estimation is also presented. An expression to evaluate the BER is also obtained, which requires the evaluation of a single integral involving the moment-generating function (MGF) of the squared norm of the vector containing the channel gains. In [7], upper and lower bound symbol error rate (SER) expressions are derived for a wireless system employing two-dimensional constellations and diversity techniques in the presence of non-ideal channel estimation. Some works analyze the performance of MIMO systems that operate in scenarios with imperfect channel estimation. In [8], the authors derive expressions to evaluate the BER for the maximal-ratio combiner (MRC), the zero-forcing (ZF), and the minimum-mean-square-error (MMSE) detectors considering quadrature-amplitude-modulations (QAM) and different practical channel estimation strategies. As a consequence, errors appear in the wireless channel estimation. In [9], the impact of imperfect channel estimation in MIMO systems using relays is analyzed, and expressions to evaluate the outage probability (OP) are derived. In this work, the imperfect channel estimation effects are modeled as external interference/noise. In [10], the impact of imperfect channel estimation and antenna correlation on the performance of massive MIMO systems is evaluated. The authors show that errors in the channel estimation generate floors in the BER curves when they are plotted as a function of the signal-to-noise ratio (SNR), and that these floors cannot be eliminated as the SNR increases. The performance of MIMO systems has typically been analyzed on Rayleigh fading channels [8; 9; 11], which emulates the random fading behavior (random signal attenuation) in scenarios with no line-of-sight (NLOS) between the transmitter and the receiver. However, in the literature, there are other statistical models that allow emulating the fading behavior in line-of-sight (LOS) scenarios. Among these models, the Rician distribution stands out [5; 12]. It is imperative to indicate that the Rayleigh distribution is a particular case of the Rician distribution.