Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

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Massive MIMO Detection Techniques: A Survey

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

Massive MIMO Systems for 5G and Beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction.

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

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.

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Overview of Precoding Techniques for Massive MIMO

In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.

Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

This paper presents a low-power coordinate rotation digital computer (CORDIC)-based reconfigurable discrete cosine transform (DCT) architecture. The main idea of this paper is based on the interesting fact that all the computations in DCT are not equally important in generating the frequency domain outputs. Considering the importance difference in the DCT coefficients, the number of CORDIC iterations can be dynamically changed to efficiently tradeoff image quality for power consumption. Thus, the computational energy can be significantly reduced without seriously compromising the image quality. The proposed CORDIC-based 2-D DCT architecture is implemented using 0.13 μm CMOS process, and the experimental results show that our reconfigurable DCT achieves power savings ranging from 22.9% to 52.2% over the CORDIC-based Loeffler DCT at the cost of minor image quality degradations.

Reconfigurable CORDIC-Based Low-Power DCT Architecture Based on Data Priority

https://onlinelibrary.wiley.com/doi/pdf/10.4218/etrij.17.0116.0732

Low-Complexity Massive MIMO Detectors Based on Richardson Method

Massive (or large-scale) multi-input multi-output (MIMO) technology becomes one of the most promising concept in 5th generation wireless system. In the uplink of a massive MIMO system, large dimensional channel matrix inversion becomes the most challenging issue of hardware implementation. In the previous work, Neumann series based matrix inversion method (Neumann method) was proposed as an approximated inversion method. However, the conventional Neumann method shows a considerable performance degradation compared to the exact matrix inversion. In this paper, we propose a 3-order Neumann method based architecture to achieve a reasonable tradeoff between detection performance and hardware complexity. In the proposed approach, only numerically dominant (diagonal and near diagonal) elements of approximated inversion matrix are judiciously used for a low complexity matrix inversion. The proposed matrix inversion hardware was implemented using 65-nm CMOS process. The experimental results show that our matrix inversion hardware shows 45% area reduction while maintaining a reasonable BER performance.

Low complexity massive MIMO detection architecture based on Neumann method

This paper presents a high-speed hardware architecture of an improved Givens rotation-based QR decomposition, named tournament-based complex Givens rotation (T-CGR). In the proposed approach, more than one pivots are selected and zero-insertion processes of Givens-rotations are performed in parallel like tournament in order to increase the throughput. As a result, the QR decomposition performance significantly increases compared to the triangular systolic array (TSA) approach. Moreover, the circuit area was reduced due to the smaller number of flip-flops for holding the computed results during the decomposition process. The proposed QR decomposition hardware was implemented using TSMC 0.25 um technology. The experimental results show that the proposed architecture achieves 73.00% speed-up over the TACR/TSA-based architecture for the 8 × 8 matrix decomposition.

High-speed tournament givens rotation-based QR Decomposition Architecture for MIMO Receiver

Conventional QR decomposition (QRD) hardware with a large size of channel matrix suffers from very low throughput and large latencies This paper presents a high speed multi-dimensional (M-D) coordinate rotation digital computer (CORDIC) based QRD architecture The novel high speed M-D architecture is enabled by exploiting multiple annihilations in a single CORDIC operation and removing data dependencies between two CORDIC operations (evaluation and application CORDIC) in Householder-based QRD process The proposed QRD architecture can compute 4×4 complex R matrix for every 8 clock cycles Our QRD hardware for 4×4 channel matrix was implemented using Samsung 013μm CMOS process, and the experimental results show that the proposed architecture achieves 474x speed-up compared to the conventional hybrid M-D based QRD

Ji-Hwan Yoon

Papers

Reconfigurable CORDIC-Based Low-Power DCT Architecture Based on Data Priority

Low-Complexity Massive MIMO Detectors Based on Richardson Method

Low complexity massive MIMO detection architecture based on Neumann method

High-speed tournament givens rotation-based QR Decomposition Architecture for MIMO Receiver

Multidimensional Householder based high-speed QR decomposition architecture for MIMO receivers