Zhongxu Wang

Bio: Zhongxu Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Detection theory & Approximation algorithm. The author has an hindex of 3, co-authored 3 publications receiving 142 citations.

Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems

Abstract: For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

Matrix Inversion-Less Signal Detection Using SOR Method for Uplink Large-Scale MIMO Systems

Abstract: For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

Low-complexity signal detection using CG method for uplink large-scale MIMO systems

Abstract: Large-scale multiple-input multiple-output (LS-MIMO) is considered as a promising key technology for future 5G wireless communications due to its very high spectrum and energy efficiency. However, one challenging problem to achieve these benefits is a practical signal detection algorithm in the uplink. In this paper, we propose a low-complexity near-optimal signal detection algorithm using the conjugate gradient (CG) method for uplink multi-user large-scale MIMO systems, which can avoid the complicated matrix inversion required by linear minimum mean square error (MMSE) signal detection algorithm. We also provide the convergence proof of the proposed scheme to guarantee its usability in practice. The analysis indicates that the proposed scheme can reduce the computational complexity by about one order of magnitude. The simulation results of the bit error rate performance verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm by using only a small number of iterations.

Massive MIMO Detection Techniques: A Survey

Abstract: 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.

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

Abstract: 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.

Overview of Precoding Techniques for Massive MIMO

Abstract: 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.

Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO

Abstract: In medium-size massive MIMO systems, the minimum mean-square-error parallel interference cancellation (MMSE-PIC)-based soft-input soft-output (SISO) detector is often used due to its relatively low complexity and good bit error rate (BER) performance. The computational complexity of MMSE-PIC for detecting a block of data is dominated by the computation of a Gram matrix and a matrix inversion. They have computational complexity of $\mathcal{O}(K^2M)$ and $\mathcal{O}(K^3)$ , respectively, where $K$ is the number of uplink users with one transmit antenna each and $M$ is the number of receive antennas at the base station. In this letter, by using an $L$ (typically $L \leq 3$ ) terms of Neumann series expansion to approximate the matrix inversion, we reduce the total computational complexity to $\mathcal{O}(LKM)$ . Compared with alternative algorithms, which focus on reducing the complexity of the matrix inversion only, the proposed method can also avoid calculating the Gram matrix explicitly and thus significantly reducing the total complexity.

High-Throughput Data Detection for Massive MU-MIMO-OFDM using Coordinate Descent

Abstract: Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables.