Lattice Sphere Detection Techniques In Block Data Transmission And Multiuser Wireless Systems

Recent years have witnessed an increase in demand for higher transmission data rates for wireless multimedia communications applications. Block Data Transmission Systems (BDTS) and Code Division Multiple Access (CDMA) are considered as efficient techniques for high data rate transmission and found in the coming generation of mobile and wireless technologies such as Long Term Evolution (LTE) systems. The Exhaustive Search (ES) detector is the optimum. Owing to its high computational load, lattice sphere detection (LSD) technique and its variants had been proposed. For the system designer, the main objective is to achieve an attractive performance-complexity tradeoff. In this research, LSD based detectors are designed for BDTS and Multi-User Detection (MUD) system. LSD searches lattice points in a sphere within a predetermined radius. In LSD, when the initial radius increases, the performance and complexity increased. This research produces exact expression for the sphere radius used in LSD technique which depends on the lattice dimension and average received power. It is well known fact that a small condition number results in a better detection performance. This research aims to reduce the condition number value to its smallest possible using the regularization methods (L1-regularization and L2-regularization), and utilizing special matrices (i.e., Hankel and Toeplitz). Sequentially, this research proposed a new detection technique which called as a near-An-LSD technique. Exact relationships between the LSD performance and condition number, and the relationship between the radius and condition number had been derived

Sixth Generation (6G)Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research direction

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

On approximate matrix inversion methods for massive MIMO detectors

Abstract Massive multiple-input multiple-output (MIMO) systems have been proposed to meet the user demands in terms of performance and quality of service (QoS). Due to the large number of antennas, detectors in massive MIMO are playing a crucial role in guaranteeing a satisfactory performance, while their complexity is also being increased. This paper considers several approximate algorithms to avoid direct matrix inversion, namely the Neumann method, the Gauss-Seidel (GS) method, the successive over-relaxation (SOR) method, the Jacobi method, the Richardson method, the optimized coordinate descent (OCD), and the conjugate gradients (CG) method. Also, this paper presents a comparison among the approximate matrix inversion methods and the minimum mean square error (MMSE). Simulation of 16×128, and 16×32 MIMO systems shows that a detector based on the GS method outperforms other detectors when the ratio of base station (BS) antennas to user terminal antennas, β, is small. On the other hand, the detector based on the SOR method outperforms the other approximate matrix inversion methods when β is large. In addition, this paper studies and recommends the setting values of relaxation parameter (ω) in the SOR and Richardson methods. It also provides a comparison among the approximate matrix inversion methods in the number of multiplications. Simulation results show that the Neumann method, the OCD method, and the CG method achieve the lowest number of multiplications while the CG method outperforms the Neumann and the OCD methods. This paper also shows that not every iteration improves the performance

Deep learning for massive MIMO uplink detectors

Abstract Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers’ performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector’s architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO

Linear massive MIMO uplink detector based on joint Jacobi and Gauss-Seidel methods

Abstract In fifth generation (5G) cellular system, massive multiple-input multiple-output (MIMO) is utilized to improve the diversity gain, reliability, link robustness, latency, and power and spectral efficiencies. However, a large number of antennas requires sophisticated signal processing to detect data. Although the detection based on maximum likelihood (ML) obtains the best performance, it is not hardware friendly because of the exponential complexity. Therefore, several iterative methods are proposed to estimate the signal without computing the inverse of equalization matrix, and hence, minimize the complexity. The Jacobi (JA) and the Gauss-Seidel (GS) methods achieve a satisfactory performance. However, large iterations’ number is in demand which produces a high computational complexity. This paper proposes a detector for massive MIMO uplink (UL) system based on the JA and GS methods. Proposed detector obtains a balance between the performance and the complexity. In this research, initialization is performed based on the JA method. After-that, the estimation is performed based on the GS method. Numerical results show that the proposed JA-GS detector outperforms the GS and the JA based detector. Moreover, proposed JA-GS based detector requires few iterations to obtain the target performance and hence, a considerable reduction in computational complexity is achieved

Overview of Precoding Techniques for Massive MIMO

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

FPGA implementation of stair matrix based massive MIMO detection

Abstract Approximate matrix inversion based methods is widely used for linear massive multiple-input multiple-output (MIMO) received symbol vector detection. Such detectors typically utilize the diagonally dominant channel matrix of a massive MIMO system. Instead of diagonal matrix, a stair matrix can be utilized to improve the error-rate performance of a massive MIMO detector. In this paper, we present very large-scale integration (VLSI) architecture and field programmable gate array (FPGA) implementation of a stair matrix based iterative detection algorithm. The architecture supports a base station with 128 antennas, 8 users with single antenna, and 256 quadrature amplitude modulation (QAM). The stair matrix based detector can deliver a 142.34 Mbps data rate and reach a clock frequency of 258 MHz in a Xilinx Virtex -7FPGA. The detector provides superior error-rate performance and higher scaled throughput than most contemporary massive MIMO detectors