The Role of Correlation in the Performance of Massive MIMO Systems

Abstract

Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

“…According to typical wireless networks, the coherence block length that is available for transmission resources consists of time and frequency symbols. These symbols represent the intervals of time and frequency during which the channel responses remain mostly constant [47][48][49][50]. Extension to orthogonal frequency-division multiple (OFDM) could be considered in the future [51,52].…”

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

“…Inaccuracies in estimating or defining the feedback of the covariance matrix can impact the system performance, thereby leading to suboptimal solutions and reduced overall system performance. Since the covariance matrix is represented by an expectation of the instantaneous channel vectors and relies on the scattering environment and the propagation geometry, it can be considered as locally stationary and varies much more slowly than the instantaneous channel of the coherence time [47][48][49]. Therefore, the channel covariance matrix can be accurately obtained in both the TDD and FDD transmission modes [50][51][52][53].…”