Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

Abstract

Scalable Multi-antenna systems are designed to gain improvement in energy and spectral efficiency under different propagation conditions by equipping the BSs with hundreds or even more antennas. A better understanding of such systems leads to overcome the essential challenges, which is important for the beneficial deployment in future networks. In wireless communication systems, channel models describe the main characteristics of the propagation environment and are essential for systems performance evaluation. This article considers a multi-cell large scale multiuser MIMO (LS-MU-MIMO) system with a physical correlated channel model and linear-based MMSE estimator and detector.A physical local scattering geometric-based stochastic channel model for dense urban and suburban scenarios is presented in this article to illustrate the impact of propagation environment on signal transmission in a general framework of antenna spatial correlation. Also, a pilot signaling-based channel estimation in the uplink scenario, the effect of pilot contamination (PC), and spatial correlation on the channel estimation are studied. The effect of PC and channel estimation on the performance of LS-MU-MIMO system is investigated by applying MMSE-based channel estimator and detector over uniform linear arrays (ULA) of BS antennas in different scattering environments. Moreover, different pilot reuse patterns are considered, and their impact on the average sum SE has been investigated. The simulation results show that LS-MU-MIMO system can provide higher performance for dense urban scenarios where the user terminals (UTs) have limited mobility and higher coherence time than suburban scenarios. Furthermore, while higher spatial correlation will result in improvement in the channel estimation quality, it contributes imperfectly to the channel hardening effect.

Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

“…The estimation of the transmitted signal vector using MMSE is given by 36 $$$$ \hat{\mathbf{s}}={\left({\mathbf{H}}^H\mathbf{H}+\frac{\sigma^2}{E_s}{\mathbf{I}}_{\mathbf{U}}\right)}^{-1}{\mathbf{H}}^H\mathbf{y}={\mathbf{A}}^{-1}\hat{\mathbf{y}}, $$$$ where $$$ {E}_s $$$ is the average symbol energy, $$$ \hat{\mathbf{y}}={\mathbf{H}}^H\mathbf{y} $$$ denotes the matched‐filter output, and $$$ \mathbf{A} $$$ stands for the MMSE filtering matrix defined as $$$$ \mathbf{A}=\mathbf{G}+\frac{\sigma^2}{E_s}{\mathbf{I}}_U, $$$$ where $$$ \mathbf{G}={\mathbf{H}}^H\mathbf{H} $$$ is the Gram matrix. Compared to the optimal ML detection algorithm, MMSE substantially reduces the computational complexity 37,38 . The main challenge for MMSE detector is inverting the matrix $$$ \mathbf{A} $$$, which requires the computational cost of cubic with respect to the number of user antennas.…”

“…Compared to the optimal ML detection algorithm, MMSE substantially reduces the computational complexity. 37,38 The main challenge for MMSE detector is inverting the matrix A, which requires the computational cost of cubic with respect to the number of user antennas. Its complexity can be reduced considerably by using the iterative methods.…”

User-Centric Cell-Free and Co-Located Cellular Large Scale MU-MIMO Systems: A Comparative Performance Study With Spatial Channel Correlation in Dense Urban Scenario

“…Since the estimated channels include some estimation error (as stated in section III), in this section, we analyze the channel estimation accuracy of the investigated Cell-Free and Co-Cellular system configurations. We adopt a normalized mean square error ‫ܧܵܯ(‬ തതതതത) as an accuracy metric for both systems, which is given as [27] ‫ܧܵܯ‬…”

Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels

“…A recently developed concept, known as cell-free large-scale MU-MIMO, provides a novel network architecture based on three well-known technologies: large-scale MU-MIMO [12][13][14][15][16][17][18], Coordinated Multi-Point (CoMP) [19], and Distributed Antenna System (DAS) [20,21]. Cell-free large-scale MU-MIMO has been proposed as a potential alternative to dividing up the coverage area into cells.…”