Download or read book Channel Estimation Overhead Reduction for Downlink FDD Massive MIMO Systems written by Abderrahmane Mayouche and published by . This book was released on 2016 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.

Download or read book Channel Estimation in TDD and FDD Based Massive MIMO Systems written by Javad Mirzaei and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells. The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression. In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific channel parameters are estimated via downlink training using a very short training signal. To efficiently estimate the channel parameters in the uplink, the underlying distribution of the channel parameters is incorporated as a prior into our estimation algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).

Download or read book Massive MIMO written by Hien Quoc Ngo and published by Linköping University Electronic Press. This book was released on 2015-01-16 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO. In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation. In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.

Download or read book Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery written by Yacong Ding and published by . This book was released on 2018 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems, where the base station (BS) is equipped with a large number of antenna elements to serve multiple user equipments. With the large number of antenna elements, the BS can perform multi-user beamforming with much narrower beamwidth, thereby simultaneously serving more users with less interference among them. Furthermore, the large antenna array results in large array gain which lowers the radiated energy. However, efficient beamforming relies on the availability of channel state information at the BS. In a frequency-division duplexing massive MIMO system, the channel estimation is challenging due to the need to estimate a high dimensional unknown channel vector, which requires large training and feedback overhead for the conventional channel estimation algorithms. Moreover, massive MIMO system with fully digital architecture, where a dedicated radio frequency chain and a high-resolution analog-to-digital converter (ADC) are connected to each antenna element, will cause too much power and hardware cost as the size of the antenna array becomes large. To reduce the training and feedback overhead, compressive sensing methods and sparse recovery algorithms are proposed to robustly estimate the downlink and uplink channel by exploiting the sparse representation of the massive MIMO channel. Previous works model this sparse representation by some predefined matrix, while in this dissertation, a dictionary learning based channel model is proposed which learns an efficient and robust representation from the data. Furthermore, a joint uplink/downlink dictionary learning framework is proposed by observing the reciprocity between the angle of arrival in uplink and the angel of departure in downlink, which enables a joint channel estimation algorithm. To save the power and hardware cost, a hardware-efficient architecture which contains both hybrid analog-digital processing and low-resolution ADCs is proposed. This hardware-efficient architecture poses significant challenges to channel estimation due to the reduced dimension and precision of the measured signal. To address the problem, the sparse nature of the channel is exploited and the transmitted data symbols are utilized as the "virtual pilots", both of which are treated in a unified Bayesian formulation. We formulate the channel estimation into a quantized compressive sensing problem utilizing the sparse Bayesian learning framework, and develop a variational Bayesian algorithm for inference. The performance of the compressive sensing can be further improved by applying a well structured sensing matrix, and we propose a sensing matrix design algorithm which can exploit the partial knowledge of the support.

Download or read book Sparse Signal Processing for Massive MIMO Communications written by Zhen Gao and published by Springer Nature. This book was released on with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Massive MIMO Systems written by Kazuki Maruta and published by MDPI. This book was released on 2020-07-03 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple-input, multiple-output (MIMO), which transmits multiple data streams via multiple antenna elements, is one of the most attractive technologies in the wireless communication field. Its extension, called ‘massive MIMO’ or ‘large-scale MIMO’, in which base station has over one hundred of the antenna elements, is now seen as a promising candidate to realize 5G and beyond, as well as 6G mobile communications. It has been the first decade since its fundamental concept emerged. This Special Issue consists of 19 papers and each of them focuses on a popular topic related to massive MIMO systems, e.g. analog/digital hybrid signal processing, antenna fabrication, and machine learning incorporation. These achievements could boost its realization and deepen the academic and industrial knowledge of this field.

Download or read book Channel Estimation and Data Detection Methods for 1 bit Massive MIMO Systems written by David Kin Wai Ho and published by . This book was released on 2022 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems. In massive MIMO, a base station (BS) is equipped with a large antenna with potentially hundreds of antennas elements, allowing many users to be served simultaneously. Unfortunately, the hardware complexity and power consumption will scale with the number of antennas. The use of one-bit analog-to-digital converters (ADCs) provides an attractive solution to solve the above issues, since a one-bit ADC consumes negligible power and complex automatic gain control (AGC) can be removed. However, the signal distortion from the severe quantization poses significant challenges to the system designer. One bit quantization effectively removes all amplitude information, which is not recoverable by an increase in signal strength. This places a bound on channel estimation performance. Since the channel model is highly nonlinear, linear detector is suboptimal compared to more sophisticated nonlinear techniques. To reduce the impairment caused by one-bit quantization, a novel antithetic dithering scheme is developed. Antithetic dither is introduced into the system to generate negative correlated noise. Efficient channel estimation algorithms are developed to exploit the induced negative correlated noise in the system. A statistical framework is developed to validate the noise reduction from negative correlated quantized output. To improve the performance of data detection, feed forward neural network based detectors are developed, performance of these detectors are analyzed, architectural modification and training techniques are employed to partially resolve issues that prevent the networks from reaching ideal maximum likelihood performance. Next, model based approaches are evaluated and the shortcomings of iterative methods that rely on the exact likelihood are identified. Iterative methods based on the exact likelihood is shown to diverge due to the increasingly large gradient at high SNR. The constant gradient induced by the sigmoid approximation is shown to increase the robustness of these methods. A structured deep learning detector based on stochastic variational inference is proposed. Stochastic estimate of the gradient is introduced to reduce complexity of the algorithm. Damping is added to improve the performance of mean field inference. Parallel processing is proposed to reduce inference time. The proposed detector is shown to outperform existing methods that do not employ a second candidate search step.

Download or read book Limited Feedback Scheme Using Tensor Decompositions for FDD Massive MIMO Systems written by Kevin Jinho Joe and published by . This book was released on 2020 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a novel limited feedback scheme for massive multiple-input multiple-output (MIMO) systems in frequency-division duplexing (FDD) wideband system. We assume that the user (UE) has knowledge of a downlink (DL) channel estimate. In order for massive MIMO systems to achieve high capacity, the base station (BS) must have the DL channel state information. Traditional feedback methods cannot work because channels for massive MIMO systems are usually too large to feedback within the coherence time. Our goal is to feedback the DL channel estimate from the UE back to the BS with as little information as possible. Our method uses two different tensor decompositions, the canonical polyadic decomposition (CPD) and the rank-(L [subscript r], L [subscript r], 1) or LL-1 block decomposition, on the DL frequency channel to estimate its parameters. By feeding back only the channel parameters, we show through simulations that our method is able to efficiently and accurately reconstruct the DL channel

Download or read book Channel Estimation in TDD Massive MIMO Systems with Subsampled Data at BS written by Yichuan Tian and published by . This book was released on 2016 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel state information at transmitter side (CSIT) is essential. Frequency division duplex (FDD) is widely employed by the most cellular systems today. However, it requires unaffordable pilot overhead and has high computational complexity. On the other hand, by exploiting the channel reciprocity using uplink pilots, the time division duplex (TDD) can overcome the overwhelming pilot training as well as the pilot feedback overhead. Considering these advantages, we propose a subsampling algorithm that can be implemented in a TDD mode. Particularly, we first exploit the intrinsic sparsity of CSIT, and then employ the Walsh-Hadamard Transform (WHT), which will subsample the received signal at BS, to perform channel estimation. Additionally, we discuss the proposed channel estimation scheme in a multicell scenario. Simulation results demonstrate that the proposed algorithm can accurately estimate channels with reduced computational complexity.

Download or read book Intelligent Data Communication Technologies and Internet of Things written by D. Jude Hemanth and published by Springer Nature. This book was released on 2022-02-28 with total page 1042 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the 5th International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI 2021), organized by JCT College of Engineering and Technology, Coimbatore, Tamil Nadu, India during 27 – 28 August 2021. This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy.

Download or read book mmWave Massive MIMO written by Shahid Mumtaz and published by Academic Press. This book was released on 2016-12-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: mmWave Massive MIMO: A Paradigm for 5G is the first book of its kind to hinge together related discussions on mmWave and Massive MIMO under the umbrella of 5G networks. New networking scenarios are identified, along with fundamental design requirements for mmWave Massive MIMO networks from an architectural and practical perspective. Working towards final deployment, this book updates the research community on the current mmWave Massive MIMO roadmap, taking into account the future emerging technologies emanating from 3GPP/IEEE. The book's editors draw on their vast experience in international research on the forefront of the mmWave Massive MIMO research arena and standardization. This book aims to talk openly about the topic, and will serve as a useful reference not only for postgraduates students to learn more on this evolving field, but also as inspiration for mobile communication researchers who want to make further innovative strides in the field to mark their legacy in the 5G arena. Contains tutorials on the basics of mmWave and Massive MIMO Identifies new 5G networking scenarios, along with design requirements from an architectural and practical perspective Details the latest updates on the evolution of the mmWave Massive MIMO roadmap, considering future emerging technologies emanating from 3GPP/IEEE Includes contributions from leading experts in the field in modeling and prototype design for mmWave Massive MIMO design Presents an ideal reference that not only helps postgraduate students learn more in this evolving field, but also inspires mobile communication researchers towards further innovation

Download or read book Digital Communication for Practicing Engineers written by Feng Ouyang and published by John Wiley & Sons. This book was released on 2019-09-04 with total page 683 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers concise, practical knowledge on modern communication systems to help students transition smoothly into the workplace and beyond This book presents the most relevant concepts and technologies of today's communication systems and presents them in a concise and intuitive manner. It covers advanced topics such as Orthogonal Frequency-Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO) Technology, which are enabling technologies for modern communication systems such as WiFi (including the latest enhancements) and LTE-Advanced. Following a brief introduction to the field, Digital Communication for Practicing Engineers immerses readers in the theories and technologies that engineers deal with. It starts off with Shannon Theorem and Information Theory, before moving on to basic modules of a communication system, including modulation, statistical detection, channel coding, synchronization, and equalization. The next part of the book discusses advanced topics such as OFDM and MIMO, and introduces several emerging technologies in the context of 5G cellular system radio interface. The book closes by outlining several current research areas in digital communications. In addition, this text: Breaks down the subject into self-contained lectures, which can be read individually or as a whole Focuses on the pros and cons of widely used techniques, while providing references for detailed mathematical analysis Follows the current technology trends, including advanced topics such as OFDM and MIMO Touches on content this is not usually contained in textbooks such as cyclo-stationary symbol timing recovery, adaptive self-interference canceler, and Tomlinson-Harashima precoder Includes many illustrations, homework problems, and examples Digital Communication for Practicing Engineers is an ideal guide for graduate students and professionals in digital communication looking to understand, work with, and adapt to the current and future technology.

Download or read book Multiuser Downlink Path based Precoding in Fdd Massive Mimo Systems Without Csi Feedback written by 許晉維 and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fundamentals of Massive MIMO written by Thomas L. Marzetta and published by Cambridge University Press. This book was released on 2016-11-17 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by pioneers of the concept, this is the first complete guide to the physical and engineering principles of Massive MIMO. Assuming only a basic background in communications and statistical signal processing, it will guide readers through key topics in multi-cell systems such as propagation modeling, multiplexing and de-multiplexing, channel estimation, power control, and performance evaluation. The authors' unique capacity-bounding approach will enable readers to carry out effective system performance analyses and develop advanced Massive MIMO techniques and algorithms. Numerous case studies, as well as problem sets and solutions accompanying the book online, will help readers put knowledge into practice and acquire the skill set needed to design and analyze complex wireless communication systems. Whether you are a graduate student, researcher, or industry professional working in the field of wireless communications, this will be an indispensable guide for years to come.

Download or read book Communications and Networking written by Qianbin Chen and published by Springer. This book was released on 2017-09-30 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNICST 209-210 constitutes the post-conference proceedings of the 11th EAI International Conference on Communications and Networking, ChinaCom 2016, held in Chongqing, China, in September 2016. The total of 107 contributions presented in these volumes are carefully reviewed and selected from 181 submissions. The book is organized in topical sections on MAC schemes, traffic algorithms and routing algorithms, security, coding schemes, relay systems, optical systems and networks, signal detection and estimation, energy harvesting systems, resource allocation schemes, network architecture and SDM, heterogeneous networks, IoT (Internet of Things), hardware design and implementation, mobility management, SDN and clouds, navigation, tracking and localization, future mobile networks.

Download or read book Massive MIMO Channel Characterization and Propagation based Antenna Selection Strategies written by Frédéric Challita and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous efforts have been made to boost wireless systems performance, however, current wireless networks are not yet able to fulfill the many gaps from 4G and requirements for 5G. Thus, significant technological breakthroughs are still required to strengthen wireless networks. For instance, in order to provide higher data rates and accommodate many types of equipment, more spectrum resources are needed and the currently used spectrum requires to be efficiently utilized. 5G, or the fifth generation of mobile networks, is initially being labeled as an evolution, made available through improvements in LTE, but it will not be long before it becomes a revolution and a major step-up from previous generations. Massive MIMO has emerged as one of the most promising physical-layer technologies for future 5G wireless systems. The main idea is to equip base stations with large arrays (100 antennas or more) to simultaneously communicate with many terminals or user equipments. Using smart pre-processing at the array, massive MIMO promises to deliver superior system improvement with improved spectral efficiency, achieved by spatial multiplexing and better energy efficiency, exploiting array gain and reducing the radiated power. Massive MIMO can fill the gap for many requirements in 5G use-cases notably industrial IOT (internet of things) in terms of data rates, spectral and energy efficiency, reliable communication, optimal beamforming, linear processing schemes and so on. However, the hardware and software complexity arising from the sheer number of radio frequency chains is a bottleneck and some challenges are still to be tackled before the full operational deployment of massive MIMO. For instance, reliable channel models, impact of polarization diversity, optimal antenna selection strategies, mutual coupling and channel state information acquisition amongst other aspects, are all important questions worth exploring. Also, a good understanding of industrial channels is needed to bring the smart industry of the future ever closer.In this thesis, we try to address some of these questions based on radio channel data from a measurement campaign in an industrial scenario using a massive MIMO setup. The thesis' main objectives are threefold: 1) Characterization of massive MIMO channels in Industry 4.0 (industrial IoT) with a focus on spatial correlation, classification and impact of cross-polarization at transmission side. The setup consists in multiple distributed user-equipments in many propagation conditions. This study is based on propagation-based metrics such as Ricean factor, correlation, etc. and system-oriented metrics such as sum-rate capacity with linear precoding and power allocation strategies. Moreover, polarization diversity schemes are proposed and were shown to achieve very promising results with simple allocation strategies. This work provides comprehensive insights on radio channels in Industry 4.0 capable of filling the gap in channel models and efficient strategies to optimize massive MIMO setups. 2) Proposition of antenna selection strategies using the receiver spatial correlation, a propagation metric, as a figure of merit. The goal is to reduce the number of radio frequency chain and thus the system complexity by selecting a set of distributed antennas. The proposed strategy achieves near-optimal sum-rate capacity with less radio frequency chains. This is critical for massive MIMO systems if complexity and cost are to be reduced. 3) Proposition of an efficient strategy for overhead reduction in channel state information acquisition of FDD (frequency-division-duplex) systems. The strategy relies on spatial correlation at the transmitter and consists in solving a set of simple autoregressive equations (Yule-Walker equations). The results show that the proposed strategy achieves a large fraction of the performance of TDD (time-division-duplex) systems initially proposed for massive MIMO.

Download or read book Proceedings of the International Conference on Paradigms of Computing Communication and Data Sciences written by Rajendra Prasad Yadav and published by Springer Nature. This book was released on 2023-02-23 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at International Conference on Paradigms of Communication, Computing and Data Sciences (PCCDS 2022), held at Malaviya National Institute of Technology Jaipur, India, during 05 – 07 July 2022. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications and data science techniques. The book is a collection of latest research articles in computation algorithm, communication and data sciences, intertwined with each other for efficiency.