In this paper, an approach of estimating signal parameters via rotational invariance technique (ESPRIT) is proposed for two-dimensional (2-D) localization of incoherently distributed (ID) sources in large-scale/massive multiple-input multiple-output (MIMO) systems. The traditional ESPRIT-based methods are valid only for one-dimensional (1-D) localization of the ID sources. By contrast, in the proposed approach the signal subspace is constructed for estimating the nominal azimuth and elevation direction-of-arrivals and the angular spreads. The proposed estimator enjoys closed-form expressions and hence it bypasses the searching over the entire feasible field. Therefore, it imposes significantly lower computational complexity than the conventional 2-D estimation approaches. Our analysis shows that the estimation performance of the proposed approach improves when the large-scale/massive MIMO systems are employed. The approximate Cramer-Rao bound of the proposed estimator for the 2-D localization is also derived. Numerical results demonstrate that albeit the proposed estimation method is comparable with the traditional 2-D estimators in terms of performance, it benefits from a remarkably lower computational complexity.

/pdf/an-esprit-based-approach-for-2-d-localization-of-34tl0otp22.pdf

An ESPRIT-Based Approach for 2-D Localization of Incoherently Distributed Sources in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is playing a crucial role in the fifth generation (5G) and beyond 5G (B5G) communication systems. Unfortunately, the complexity of massive MIMO systems is tremendously increased when a large number of antennas and radio frequency chains (RF) are utilized. Therefore, a plethora of research efforts has been conducted to find the optimal precoding algorithm with lowest complexity. The main aim of this paper is to provide insights on such precoding algorithms to a generalist of wireless communications. The added value of this paper is that the classification of massive MIMO precoding algorithms is provided with easily distinguishable classes of precoding solutions. This paper covers linear precoding algorithms starting with precoders based on approximate matrix inversion methods such as the truncated polynomial expansion (TPE), the Neumann series approximation (NSA), the Newton iteration (NI), and the Chebyshev iteration (CI) algorithms. The paper also presents the fixed-point iteration-based linear precoding algorithms such as the Gauss-Seidel (GS) algorithm, the successive over relaxation (SOR) algorithm, the conjugate gradient (CG) algorithm, and the Jacobi iteration (JI) algorithm. In addition, the paper reviews the direct matrix decomposition based linear precoding algorithms such as the QR decomposition and Cholesky decomposition (CD). The non-linear precoders are also presented which include the dirty-paper coding (DPC), Tomlinson-Harashima (TH), vector perturbation (VP), and lattice reduction aided (LR) algorithms. Due to the necessity to deal with a high consuming power by the base station (BS) with a large number of antennas in massive MIMO systems, a special subsection is included to describe the characteristics of the peak-to-average power ratio precoding (PAPR) algorithms such as the constant envelope (CE) algorithm, approximate message passing (AMP), and quantized precoding (QP) algorithms. This paper also reviews the machine learning role in precoding techniques. Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. Therefore, this paper presents the application of small-scale MIMO precoding techniques for massive MIMO. This paper demonstrates the precoding schemes in promising multiple antenna technologies such as the cell-free massive MIMO (CF-M-MIMO), beamspace massive MIMO, and intelligent reflecting surfaces (IRSs). In-depth discussion on the pros and cons, performance-complexity profile, and implementation solidity is provided. This paper also provides a discussion on the channel estimation and energy efficiency. This paper also presents potential future directions in massive MIMO precoding algorithms.

/pdf/overview-of-precoding-techniques-for-massive-mimo-5e17cy4wxm.pdf

Overview of Precoding Techniques for Massive MIMO

Fast and continues development Wireless communication requires enhancement of capacity of system. To improve the capacity of system multiuser multiple inputs multiple outputs (MU-MIMO) is considered. It has ability system capacity improvement without additional bandwidth. In the implementation of MU-MIMO Multi User Interference (MUI) created and this interference degrade the system performance. Thus to minimize or suppress the interference, precoding technique used before signal transmission. Due to low complexity linear precoding preferred. For the massive system dimension, an appropriate precoding algorithm designing is difficult with less computing complexity and better error rate performance simultaneously over fading channels. This paper convey sum rate performance and bit error rate (BER) performance of linear precoding techniques above fading channels with assuming that the transmitter has perfect channel state information (CSI). To valuation of the bit error rate performance, different fading surroundings namely, Rician, Nakagami, and Rayleigh fading channel distributions offered.

Performance Analysis of Linear Precoding Techniques over the Fading Channel for MU-MIMO

In this paper, we investigate low-complexity user scheduling schemes for the downlink (DL) massive multiuser multiple-input multiple-output (MU-MIMO) system, where an M-antenna (M is very large) base station (BS) serves K (K > 2) N-antenna (N > 1) users with linear transceivers. Establishing the theoretical foundation of our scheduling schemes, we first investigate the asymptotic sum-rates of this system with both singular value decomposition (SVD) and regularized zero-forcing (RZF) precoders. In particular, we assume that both M and N go to infinity with a bounded ratio to perform the theoretical derivations with random matrix theory (RMT). In addition, the influence of imperfect channel estimation is also considered. Interestingly, the analysis indicates the existence of an optimal number of served users K1 in terms of asymptotic sum-rate, which motivates our low complexity user scheduling schemes. Unlike the conventional schemes with complicated computations and instantaneous channel state information, our user scheduling schemes are based on semi-analytical solutions with only statistic CSI. Simulation results validate our theoretical derivations and show the advantages of our schemes in the massive MU-MIMO system.

Low-complexity user scheduling in the downlink massive MU-MIMO system with linear precoding

In this study, the authors present asymptotic capacity analysis of block diagonalisation (BD)-precoding and BD with space-time block code schemes for future wireless communication systems. Assuming that the number of base station antennas N and the number of antennas per user M are large while their ratio remains bounded, the capacity expressions for these two schemes are derived in both cases of perfect channel state information (CSI) and imperfect CSI, respectively. Based on this, the authors obtain the optimal number of user terminals K to be served by the system. Closed-form expressions of the optimum training length based on the basic system parameters are derived for practical implementation. The accuracy of the analysis is validated by numerical results with different parameter settings. It has been shown that the capacity expressions are accurate even for small M.

Performance analysis of massive multiuser multiple-input multiple-output systems with block diagonalisation

In this paper, an undesired fact for multiuser (MU) MIMO (MU-MIMO) systems using block diagonalization is presented, i.e., the increment of user number (K) results in the degradation of sum rate. This fact is caused by the opposition between two main factors determining the sum rate, namely, MU gain and transmit diversity. The MU gain guarantees a high order of degrees of freedom (DoFs) on system level but decreases the rate per user, while, for the transmit diversity, it is exactly opposite. Therefore, the tradeoff between the two factors is established to quantify how the two factors impact on the sum rate. Based on the tradeoff, the optimal K that maximizes the sum rate is further obtained. Additionally, it is pointed out that the sum rate can be improved substantially by only adding a few antennas at the base station when the system is fully loaded. The derivations are under large-scale system assumption, and being implemented on different precoders. Numerical simulations verify the tightness and accuracy of our asymptotic results for both large-scale and conventional systems.

Multiuser MIMO using block diagonalization: How many users should be served?

In this paper, we proposed a vector perturbation precoding approach based on reactive tabu search (RTS) for large multiuser MIMO (MU-MIMO) systems, where ‘large’ means tens to hundreds of transmit antennas (N t ) at base station simultaneously serving almost the same number of user terminals (UTs) (N u ). By exploiting RTS, contrast to the conventional algorithm, the proposed approach can efficiently escape from poor local minima and has the relatively low complexity. Additionally, for the error bit rate (BER), this proposed approach also has the diversity increasing with N t and N u , thus making it suitable for large MU-MIMO systems both in terms of complexity and performance.

A Low-Complexity Vector Perturbation Precoding Approach Based on Reactive Tabu Search for Large Multiuser MIMO Systems

In this paper, the large-scale MU-MIMO system is considered where a base station (BS) with extremely large number of antennas (N) serves relatively less number of users (K). In order to achieve largest sum rate, it is proven that the amount of users must be limited such that the number of antennas at the BS is preponderant over that of the antennas at all the users. In other words, the antennas at the BS should be excess. The extra antennas at the BS are no longer just an optional approach to enhance the system performance but the prerequisite to the largest sum rate. Based on this factor, for a fixed N, the optimal K that maximizes the sum rate is further obtained. Additionally, it is also pointed out that the sum rate can be substantially improved by only adding a few antennas at the BS when the system is N=KM with M denoting the antennas at each user. The derivations are under the assumption of N and M going to infinity, and being implemented on different precoders. Numerical simulations verify the tightness and accuracy of our asymptotic results even for small N and M.

pdf/large-scale-mu-mimo-it-is-necessary-to-deploy-extra-antennas-2rery4p3z3.pdf

Large-scale MU-MIMO: It Is Necessary to Deploy Extra Antennas at Base Station.

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Papers

Multiuser MIMO using block diagonalization: How many users should be served?

A Low-Complexity Vector Perturbation Precoding Approach Based on Reactive Tabu Search for Large Multiuser MIMO Systems

Large-scale MU-MIMO: It Is Necessary to Deploy Extra Antennas at Base Station.