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Proceedings ArticleDOI

Performance comparison of massive MIMO and conventional MIMO using channel parameters

22 Mar 2017-pp 1808-1812
TL;DR: Estimating the channel parameters for conventional MIMO and massive MIMo based on training-based and blind channel estimation techniques wherein the performance of both is compared is compared.
Abstract: Multiple-input multiple-output (MIMO) technology is becoming mature in wireless communication systems. It has led to third and fourth generation wireless systems, which has been providing good range, reliability and higher data rates. For the increased demand of much higher data rates, coverage, spectral efficiency, capacity and reduced latency, the evolution of the next generation i.e., the fifth generation technology is necessary. Massive MIMO technology is one of the most promising solution for the above-mentioned challenge. In massive MIMO, the base station is incorporated with hundreds to thousands of antenna array wherein the degrees of freedom can be exploited and the energy can be efficiently used due to the fact that the extra antennas at the base station helps focus the energy into the smaller regions of space. To reap the benefits provided by the extra antennas, the channel information is necessary which makes it possible to have a reliable communication. Therefore, to acquire the channel knowledge, channel state information is required at the base station and estimating the channel parameters plays an important role. In this paper, we concentrate on estimating the channel parameters for conventional MIMO and massive MIMO based on training-based and blind channel estimation techniques wherein the performance of both is compared.
Citations
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Journal ArticleDOI
TL;DR: This paper discusses 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, and presents recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms.
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.

262 citations


Cites background from "Performance comparison of massive M..."

  • ...Massive MIMO is a scaled up version of the conventional small scale MIMO systems [8], [9]....

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  • ...Massive MIMO systems [8], [9] with a large number of antennas (up to hundreds) at the base station (BS) or access point are a natural extension of the conventional small-scale MIMO technology....

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Journal ArticleDOI
TL;DR: In this paper, the authors provide insights on linear precoding algorithms for massive MIMO systems and discuss the performance and energy efficiency of the precoders. And they also present potential future directions of linear precoder algorithms.
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.

64 citations

Journal ArticleDOI
TL;DR: The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches.
Abstract: With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

61 citations

Journal ArticleDOI
28 Mar 2020-Entropy
TL;DR: Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications and when the ratio between the user antennas and base station antennas is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance.
Abstract: Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors' design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas ( β ) is close to 1, iterative matrix inversion methods are not attaining a good detector's performance.

16 citations


Cites background from "Performance comparison of massive M..."

  • ...M-MIMO or large scale MIMO, is an expansion of the ordinary small scale MIMO systems [41,42] where large number of antennas at the BS avails concurrently numerous users with an elasticity to select what users to schedule for reception at any moment....

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Proceedings ArticleDOI
01 Dec 2019
TL;DR: Numerical results show that a detector based on approximate matrix inversion methods outperforms the detectors based on the AMP algorithm and it also requires less processing time compared to the GS and the SOR methods.
Abstract: Massive multiple-input multiple-output (MIMO) is a promising technology to support high data rate in fifth generation (5G) wireless communications system. Design of massive MIMO detector is not a trivial task due to a large number of antennas. This paper studies a detector based on approximate matrix inversion methods, namely, the Gauss-Seidel (GS), the successive over relaxation (SOR), and the approximate message passing (AMP) algorithm. Numerical results show that a detector based on approximate matrix inversion methods outperforms the detector based on the AMP algorithm. In addition, the convergence rate of the AMP based detector is higher than others and it also requires less processing time compared to the GS and the SOR methods.

6 citations

References
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Journal ArticleDOI
TL;DR: While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
Abstract: Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

6,184 citations


"Performance comparison of massive M..." refers background in this paper

  • ...Therefore, the number of orthogonal pilot sequences required for the channel estimation depends on the number of users and not on the number of transmitters [4]....

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Journal ArticleDOI
TL;DR: This overview article identifies 10 myths of Massive MIMO and explains why they are not true, and asks a question that is critical for the practical adoption of the technology and which will require intense future research activities to answer properly.
Abstract: Wireless communications is one of the most successful technologies in modern years, given that an exponential growth rate in wireless traffic has been sustained for over a century (known as Cooper’s law). This trend will certainly continue, driven by new innovative applications; for example, augmented reality and the Internet of Things. Massive MIMO has been identified as a key technology to handle orders of magnitude more data traffic. Despite the attention it is receiving from the communication community, we have personally witnessed that Massive MIMO is subject to several widespread misunderstandings, as epitomized by following (fictional) abstract: “The Massive MIMO technology uses a nearly infinite number of high-quality antennas at the base stations. By having at least an order of magnitude more antennas than active terminals, one can exploit asymptotic behaviors that some special kinds of wireless channels have. This technology looks great at first sight, but unfortunately the signal processing complexity is off the charts and the antenna arrays would be so huge that it can only be implemented in millimeter-wave bands.” These statements are, in fact, completely false. In this overview article, we identify 10 myths and explain why they are not true. We also ask a question that is critical for the practical adoption of the technology and which will require intense future research activities to answer properly. We provide references to key technical papers that support our claims, while a further list of related overview and technical papers can be found at the Massive MIMO Info Point: http://massivemimo. eu

1,040 citations


"Performance comparison of massive M..." refers methods in this paper

  • ...Therefore, to overcome this, it is desirable to incorporate time division duplexing (TDD) method, where the estimation relies on reciprocity of the channel [3]....

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Proceedings Article
03 Mar 2015
TL;DR: In this paper, the linear minimum mean mean squared error (LMMSE) channel estimate is proposed to reduce interference in the training phase and thus reduce the impact of pilot-contamination.
Abstract: Channel estimation is a crucial part of massive MIMO systems and without accurate channel state information, the promising array and multiplexing gains cannot be achieved. In typical propagation environments, the linear minimum mean squared error (LMMSE) channel estimate significantly outperforms the simple least squares estimate. This is due to the fact that LMMSE estimation can reduce interference in the training phase and thus reduce the impact of pilot-contamination. Unfortunately, LMMSE estimation comes at a high computational cost and is thus prohibitive for a large scale system. We review existing methods for approximate low-complexity LMMSE estimation and show that it is crucial to consider the estimation of the covariance matrices when designing the estimator. We further propose a highly efficient DFT based approximation of the LMMSE estimator. Finally, the performance of the different estimators is evaluated by system level simulations.

22 citations


"Performance comparison of massive M..." refers methods in this paper

  • ...out on massive MIMO for channel estimation which is in regard to the TBCE using different algorithms and by comparing their performances based on the number of antennas and spectral efficiency [7], [8]....

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Proceedings ArticleDOI
11 Feb 2014
TL;DR: With a large number of antennas, unequal power allocation facilitated by comb arrangement can give large gains over alternative pilot arrangements and this spectral efficiency increase is particularly important with LS estimation and as the number of base station antennas grows large.
Abstract: For pilot sequence based multiple input multiple output (MIMO) channel estimation, the arrangements of pilot symbols, such as the block or comb type arrangement, is known to play an important role In this paper we compare the performance of block and comb pilot symbol patterns in terms of uplink mean square error (MSE) and spectral efficiency when the receiver at the base station employs least square (LS) or minimum mean square error (MMSE) channel estimation and MMSE equalizer for uplink data reception For this system, we derive a closed form solution for the MSE and spectral efficiency that allows us to obtain exact results for an arbitrary number of antennas Our key observation is that the comb pilot arrangement allows for unequal pilot-data power allocation in the frequency domain, which leads to a significant spectral efficiency increase This spectral efficiency increase is particularly important with LS estimation and as the number of base station antennas grows large It also gives noticeable gains with MMSE estimation Our main conclusion is that with a large number of antennas, unequal power allocation facilitated by comb arrangement can give large gains over alternative pilot arrangements

16 citations


"Performance comparison of massive M..." refers methods in this paper

  • ...out on massive MIMO for channel estimation which is in regard to the TBCE using different algorithms and by comparing their performances based on the number of antennas and spectral efficiency [7], [8]....

    [...]

Journal ArticleDOI
TL;DR: Simulation results indicate that channel estimator has been working on purpose in MIMO-OFDM system with antenna scheme of 2x2 and 2x4 as well, and Rectangular shaping filter assumption in time domain is used for the channel approximation.
Abstract: IEEE 802.11n is the latest development of IEEE 802.11 WLAN communication standard that provides higher significant throughput than IEEE 802.11a/g. With various of transmission channels in wireless communication, has decreased performance of the receiver antennas caused by noise interference and fading channel. Hence, there is a need to analyze channel estimation method to estimate and discover the real condition of channel information between transmitter and receiver for IEEE 802.11n WLAN communication standard. This research simulated the channel estimator using minimum mean squared error (MMSE) algorithm in MIMOOFDM systems with 2x2 and 2x4 schemes. Rectangular shaping filter assumption in time domain is used for the channel approximation due to the multipath Rayleigh fading channel distribution. System performance value is shown in channel impulse response of Tx transmitter to Rx receiver. The simulation results indicate that channel estimator has been working on purpose in MIMO-OFDM system with antenna scheme of 2x2 and 2x4 as well.

7 citations


"Performance comparison of massive M..." refers methods in this paper

  • ...[10] shows the work on 2×2 MIMO and 2×4 MIMO using MMSE estimator where the channel impulse response is compared with the actual channel....

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Which channel is designated as the VHF follow on communications channel?

Therefore, to acquire the channel knowledge, channel state information is required at the base station and estimating the channel parameters plays an important role.