Massive multiple-input multiple-output (MIMO) arrays have emerged as pivotal technology for 5G wireless communication systems, finding widespread implementation and deployment. However, despite their significant potential, the performance gains achieved in practical environments do not consistently scale with the accompanying rise in hardware costs. To address this issue, we delve into the design of rectangular array structures for massive MIMO with varying parameters. The core idea is to optimize the array structure to suit diverse propagation characteristics. Our approach treats the massive MIMO array as a spatial sampling system. A 2-D Fourier transform concerning the elevation and azimuth steering factors is employed to derive the angular spectrum of incoming signals at the base station. Building on spatial spectrum analysis, we unveil the relationship between antenna array parameters, such as the number of antennas and the vertical/horizontal antenna spacings, and the spatial resolution and spectral aliasing inherent to the massive MIMO system. Furthermore, we investigate how array structure parameters impact channel capacity in multiuser scenarios and propose effective strategies for enhancing capacity while mitigating aliasing through parameter adjustments. Finally, we present numerical results that validate the effectiveness of our proposed approaches. The outcomes of this study establish a solid foundation for optimizing the design, deployment, and spatial resource allocation of practical massive MIMO systems.

In massivemultiple-input multiple-output(MIMO) systems, block diagonalization-based precoding methods are employed to mitigate interference among users by relying on channel state information. However, as the number of antennas increases, the task of eigenvalue decomposition or matrix inversion for the channel covariance matrix becomes progressively challenging. In this paper, we propose an equilibrium-equation-based recursive channel principal component tracking algorithm specifically designed for linear precoding inJoint Spatial Division and Multiplexing(JSDM) systems. Unlike many existing algorithms that depend on gradient formulations and the choice of step size, our proposed recursive tracking algorithm operates without the need for a step size, significantly enhancing convergence speed and learning performance. We also derive the adaptive forgetting factor, which improves the convergence capability. Additionally, we provide a mathematical analysis of the algorithm’s convergence performance, mean deviation, and learning curve. Finally, we implement various precoding strategies to a downlink channel in a massive MIMO JSDM system, leveraging the channel’s principal components. Our simulations conclusively demonstrate that the proposed algorithm outperforms traditional tracking algorithms, while principal component-based precoding effectively enhances spectral efficiency.

The present article proposes a novel adaptive algorithm with an optimal diagonal step size matrix for multi-dimensional channel principal component tracking in two dimensional massive multiple-input and multiple-output (M-MIMO) systems. First, we prove that the weighted subspace algorithm globally converges to the stationary stochastic process’ major eigenvectors. Then, using the maximum likelihood criterion, we optimize the weight coefficient matrix and derive the convergent condition for the step size range in order to maintain the algorithm stability. To accelerate the convergence, we initially suggest the diagonal step size matrix for multi-dimensional eigenvector tracking. Simultaneously, an optimal diagonal step size matrix is derived, which not only accelerates the convergence speed distinctively but also improves the tracking of multi-dimensional eigenvectors. Moreover, the transient behavior during the adaptation process is investigated in a straight-forward way and the relationship between the convergence time constant and the eigenvalues of the received signals is uncovered. Finally, simulations reveal that the proposed approach outperforms established algorithms such as Oja$^{\prime}\text{s}$, Delmas and gradient descent algorithms. This approach establishes a sound foundation for tracking channel state information in M-MIMO systems with great mobility.

In this paper, a fast adaptive Principal Component Analysis (PCA) based scheme is proposed to estimate and track the channel state information (CSI) of two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular array (URA). First, the signals received online at the base station (BS) are used to estimate and track the principal components. Then, based on the estimated signal eigenvectors, a 2D unitary estimating signal parameters via rotational invariance technique (ESPRIT) algorithm is introduced to jointly estimate and track the channel coefficients which include the direction of arrival (DoA) of elevation and azimuth angles and the channel gain corresponding to each resolvable path of the channel. In order to improve the tracking speed, an optimal step size is derived which can accelerate the convergence speed of channel tracking significantly. Since the 2D unitary ESPRIT algorithm is applied, the proposed method can reduce computational complexity by converting the complex data matrix to the real one. Simulation results are provided to verify the estimation and tracking accuracy of the proposed scheme.

Principle component analysis (PCA) can be used to estimate the eigenvalues and eigenvectors of high dimensional data set with low complexity. By using this instinct feature, an adaptive PCA based channel parameter estimation and tracking method is proposed for two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular arrays (URAs) of antennas in this paper. The online received data is used to estimate and track the channel parameters (including the direction of arrival angles and the path gain). Since the adaptive PCA method is used, the proposed scheme has low complexity and high accuracy for estimation and tracking which are verified via the numerical simulations.

In this paper, a low complexity channel parameter estimation method is proposed for two-dimensional (2D) uniform rectangular array (URA) massive multiple-input and multiple-output (MIMO) systems. Instead of assuming independent fading between different transmit-receive antenna pairs, a physical channel which models the realistic scattering environment via the angles and gains associated with different propagation paths is studied. A novel 2D Fourier transform (FT) based on elevation and azimuth steering factors is designed to derive the spatial spectrum distribution of received signals at base-station (BS). Accordingly, the received data matrices are used to estimate the channel parameters, which includes the direction of arrival (DOA) angles and the channel gains respective to each resolvable path. Since the channel coefficients are estimated from the DOA perspective and the proposed 2D FT can be realized by Fast-Fourier-Transform (FFT), the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme.

In order to meet the challenge of increasing data-rate demand as well as the form factor limitation of the base station (BS), 3-D massive multiple-input multiple-output (MIMO) technology has been introduced as one of the enabling technologies for fifth generation mobile cellular systems. In 3-D massive MIMO systems, a BS will rely on the uplink sounding signals from mobile stations to figure out the spatial information for downlink MIMO operations. Accordingly, multi-dimensional parameter estimation of a MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the angle and delay estimation for 3-D massive MIMO systems under a parametric channel modeling. To be specific, we first introduce separate low complexity time delay and angle estimation algorithms based on unitary transformation, and analytically characterize the mean squared errors (MSEs) of these estimations for massive MIMO systems. Then, a matrix-based estimation of signal parameters via rotational invariance technique algorithm is applied to jointly estimate the delay and the angles where the MSEs are also analytically characterized. Our results show that the antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance. Simulation results suggest that the characterized MSEs match well with the simulated ones.

Mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, two-dimensional (2D) “massive MIMO” has been proposed as one of the enabling technologies for future wireless systems. In 2D “massive MIMO” systems, a base station will rely on the uplink sounding signals to figure out the downlink spatial channel information to perform MIMO precoding. Accordingly, direction-of-arrival (DoA) estimation of the underlying three-dimensional (3D) channel at the base station becomes essential for 2D “massive MIMO” systems to realize the predicted capacity gains. In this paper, we will analyze the performance of DoA estimation based on ESPRIT methods and study its impact on the capacity of 2D “massive MIMO” systems. To be specific, for ESPRIT-type algorithms, we will derive the closed-form expressions for the mean square errors of the elevation and azimuth angle estimations. These results will be used to obtain design intuitions for 2D antenna arrays at the base station as well as the capacity of the underlying 2D “massive MIMO” systems.

This paper presents an adaptive sub-carrier and power allocation scheme for orthogonal frequency division multiple access (OFDMA) systems according to their different quality of service (QoS) requirements and traffic type. The algorithm maximized the transmission data rate while satisfying total power constraint and a certain bit error rate (BER) requirement. A greedy algorithm known to be the most efficient algorithm for this problem can provide a high quality optimal solution, but has the disadvantage of incurring a long computation time. This problem should be solved in a real-time environment. The proposed algorithm not only avoids the high complexity but also provides considerable universality and flexibility for both the fixed rate voice data and variable rate multimedia data of the broadband wireless communication. It mainly consists of two steps. The first is the allocation of sub-carriers and power alternately to the real-time user. The second is the residual resource distribution to the non-real-time users. The simulation results demonstrate that the scheme has computational advantages over the conventional algorithms while providing the QoS guarantee.