Download or read book Reduced Complexity Detection for Massive MIMO OFDM Wireless Communication Systems written by Ali Jaber Abdulwahab Al-Askery and published by . This book was released on 2017 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Low complexity Data Detection for Multiple input Multiple output Wireless Communication Systems written by Fan Jiang and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The tradeoff between the computational complexity and system performance in multipleinput multiple-output (MIMO) wireless communication systems is critical to practical applications. In this dissertation, we investigate efficient low-complexity data detection schemes from conventional small-scale to recent large-scale MIMO systems, with the targeted applications in terrestrial wireless communication systems, vehicular networks, and underwater acoustic communication systems. In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA) systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition (SQRD) based turbo equalization scheme under imperfect channel estimation. We demonstrate the performance enhancement of the proposed scheme over the conventional minimum mean-square error (MMSE) based turbo equalization scheme in terms of system bit error rate (BER) and convergence performance. Furthermore, by jointly considering channel estimation error and the a priori information from the channel decoder, we develop low-complexity turbo equalization schemes conditioned on channel estimate for MIMO systems. Our proposed methods generalize the expressions used for MMSE and MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special cases. In addition, we extend the SQRD-based soft interference cancelation scheme to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show an improved system BER performance of the proposed turbo detection scheme over the conventional MMSE-based detection scheme. In the large-scale MIMO scenario, we focus on low-complexity detection schemes because computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive MIMO detection schemes. We demonstrate the applicability of the stair matrix through the study of the convergence conditions. We then investigate the system performance and demonstrate that the convergence rate and the system BER are much improved over the diagonal matrix based approach with the same system configuration. We further investigate low-complexity and fast processing detection schemes for massive MIMO systems where a block diagonal matrix is utilized in the development. Using a parallel processing structure, the processing time can be much reduced. We investigate the convergence performance through both the probability that the convergence conditions are satisfied and the convergence rate, and evaluate the system performance in terms of computational complexity, system BER, and the overall processing time. Using our proposed approach, we extend the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic (UWA) communications. By utilizing a recently proposed computational efficient statistic UWA channel model, we show that the proposed scheme can effectively approach the system performance of the original Gauss-Seidel method, but with much reduced processing delay.

Download or read book Large MIMO Systems written by A. Chockalingam and published by Cambridge University Press. This book was released on 2014-02-06 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This exclusive coverage of the opportunities, technological challenges, solutions, and state of the art of large MIMO systems provides an in-depth discussion of algorithms for large MIMO signal processing, suited for large MIMO signal detection, precoding and LDPC code designs. An ideal resource for researchers, designers, developers and practitioners in wireless communications.

Download or read book Low Complexity MIMO Detection written by Lin Bai and published by Springer Science & Business Media. This book was released on 2012-01-08 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Low Complexity MIMO Detection introduces the principle of MIMO systems and signal detection via MIMO channels. This book systematically introduces the symbol detection in MIMO systems. Includes the fundamental knowledge of MIMO detection and recent research outcomes for low complexity MIMO detection.

Download or read book MIMO OFDM Wireless Communications with MATLAB written by Yong Soo Cho and published by John Wiley & Sons. This book was released on 2010-08-20 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). In MIMO-OFDM Wireless Communications with MATLAB®, the authors provide a comprehensive introduction to the theory and practice of wireless channel modeling, OFDM, and MIMO, using MATLAB® programs to simulate the various techniques on MIMO-OFDM systems. One of the only books in the area dedicated to explaining simulation aspects Covers implementation to help cement the key concepts Uses materials that have been classroom-tested in numerous universities Provides the analytic solutions and practical examples with downloadable MATLAB® codes Simulation examples based on actual industry and research projects Presentation slides with key equations and figures for instructor use MIMO-OFDM Wireless Communications with MATLAB® is a key text for graduate students in wireless communications. Professionals and technicians in wireless communication fields, graduate students in signal processing, as well as senior undergraduates majoring in wireless communications will find this book a practical introduction to the MIMO-OFDM techniques. Instructor materials and MATLAB® code examples available for download at www.wiley.com/go/chomimo

Download or read book Data Detection for MIMO OFDM Systems with Reduced Complexity in a Frequency Selective Environment written by Nora Kraska 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 Signal Processing Channel Estimation and Link Adaptation in MIMO OFDM Systems written by Jianjun Ran and published by Cuvillier Verlag. This book was released on 2008 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book MIMO OFDM for LTE WiFi and WiMAX written by Lajos Hanzo and published by John Wiley & Sons. This book was released on 2012-01-03 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: MIMO-OFDM for LTE, WIFI and WIMAX: Coherent versus Non-Coherent and Cooperative Turbo-Transceivers provides an up-to-date portrayal of wireless transmission based on OFDM techniques augmented with Space-Time Block Codes (STBCs) and Spatial-Division Multiple Access (SDMA). The volume also offers an in-depth treatment of cutting-edge Cooperative Communications. This monograph collates the latest techniques in a number of specific design areas of turbo-detected MIMO-OFDM wireless systems. As a result a wide range of topical subjects are examined, including channel coding and multiuser detection (MUD), with a special emphasis on optimum maximum-likelihood (ML) MUDs, reduced-complexity genetic algorithm aided near-ML MUDs and sphere detection. The benefits of spreading codes as well as joint iterative channel and data estimation are only a few of the radical new features of the book. Also considered are the benefits of turbo and LDPC channel coding, the entire suite of known joint coding and modulation schemes, space-time coding as well as SDM/SDMA MIMOs within the context of various application examples. The book systematically converts the lessons of Shannon's information theory into design principles applicable to practical wireless systems; the depth of discussions increases towards the end of the book. Discusses many state-of-the-art topics important to today's wireless communications engineers. Includes numerous complete system design examples for the industrial practitioner. Offers a detailed portrayal of sphere detection. Based on over twenty years of research into OFDM in the context of various applications, subsequently presenting comprehensive bibliographies.

Download or read book MIMO Processing for 4G and Beyond written by Mario Marques da Silva and published by CRC Press. This book was released on 2016-04-19 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: MIMO Processing for 4G and Beyond: Fundamentals and Evolution offers a cutting-edge look at multiple-input multiple-output (MIMO) signal processing, namely its detection (in both time and frequency domains) and precoding. It examines its integration with OFDM, UWB, and CDMA, along with the impact of these combinations at the system level. Massive M

Download or read book Low Complexity MIMO Receivers written by Lin Bai and published by Springer Science & Business Media. This book was released on 2014-03-13 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple-input multiple-output (MIMO) systems can increase the spectral efficiency in wireless communications. However, the interference becomes the major drawback that leads to high computational complexity at both transmitter and receiver. In particular, the complexity of MIMO receivers can be prohibitively high. As an efficient mathematical tool to devise low complexity approaches that mitigate the interference in MIMO systems, lattice reduction (LR) has been widely studied and employed over the last decade. The co-authors of this book are world's leading experts on MIMO receivers, and here they share the key findings of their research over years. They detail a range of key techniques for receiver design as multiple transmitted and received signals are available. The authors first introduce the principle of signal detection and the LR in mathematical aspects. They then move on to discuss the use of LR in low complexity MIMO receiver design with respect to different aspects, including uncoded MIMO detection, MIMO iterative receivers, receivers in multiuser scenarios, and multicell MIMO systems.

Download or read book Efficient Detection and Scheduling for MIMO OFDM Systems written by Wei Liu and published by . This book was released on 2012 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple-input multiple-output (MIMO) antennas can be exploited to provide high data rate using a limited bandwidth through multiplexing gain. MIMO combined with orthogonal frequency division multiplexing (OFDM) could potentially provide high data rate and high spectral efficiency in frequency-selective fading channels. MIMO-OFDM technology has been widely employed in modern communication systems, such as Wireless Local Area Network (WLAN), Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). However, most of the conventional schemes either are computationally prohibitive or underutilize the full performance gain provided by the inherent merits of MIMO and OFDM techniques. In the first part of this dissertation, we firstly study the channel matrix inversion which is commonly required in various MIMO detection schemes. An algorithm that exploits second-order extrapolation in the time domain is proposed to efficiently reduce the computational complexity. This algorithm can be applied to both linear detection and non-linear detection such as ordered successive interference cancellation (OSIC) while maintaining the system performance. Secondly, we study the complexity reduction for Lattice Reduction Aided Detection (LRAD) of MIMO-OFDM systems. We propose an algorithm that exploits the inherent feature of unimodular transformation matrix that remains the same for relatively highly correlated frequency components. This algorithm effectively eliminates the redundant brute-force lattice reduction iterations among adjacent subcarriers. Thirdly, we analyze the impact of channel coherence bandwidth on two LRAD algorithms. Analytical and simulation results demonstrate that carefully setting the initial calculation interval according to the coherence bandwidth is essential for both algorithms. The second part of this dissertation focuses on efficient multi-user (MU) scheduling and coordination for the uplink of WLAN that uses MIMO-OFDM techniques. On one hand, conventional MU-MIMO medium access control (MAC) protocols require large overhead, which lowers the performance gain of concurrent transmissions rendered by the multi-packet reception (MPR) capability of MIMO systems. Therefore, an efficient MU-MIMO uplink MAC scheduling scheme is proposed for future WLAN. On the other hand, single-user (SU) MIMO achieves multiplexing gain in the physical (PHY) layer and MU-MIMO achieves multiplexing gain in the MAC layer. In addition, the average throughput of the system varies depending on the number of antennas and users, average payload sizes, and signal-to-noise-ratios (SNRs). A comparison on the performance between SU-MIMO and MU-MIMO schemes for WLAN uplink is hence conducted. Simulation results indicate that a dynamic switch between the SU-MIMO and MU-MIMO is of significance for higher network throughput of WLAN uplink.

Download or read book Massive MIMO Detection Algorithm and VLSI Architecture written by Leibo Liu and published by Springer. This book was released on 2019-02-20 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to a reconfigurable chip architecture for future wireless communication systems, such as 5G and beyond. The proposed architecture perfectly meets the demands for future mobile communication solutions to support different standards, algorithms, and antenna sizes, and to accommodate the evolution of standards and algorithms. It employs massive MIMO detection algorithms, which combine the advantages of low complexity and high parallelism, and can fully meet the requirements for detection accuracy. Further, the architecture is implemented using ASIC, which offers high energy efficiency, high area efficiency and low detection error. After introducing massive MIMO detection algorithms and circuit architectures, the book describes the ASIC implementation for verifying the massive MIMO detection. In turn, it provides detailed information on the proposed reconfigurable architecture: the data path and configuration path for massive MIMO detection algorithms, including the processing unit, interconnections, storage mechanism, configuration information format, and configuration method.

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 Spatial Resource Allocation in Massive MIMO Communications written by Trinh Van Chien and published by Linköping University Electronic Press. This book was released on 2019-12-09 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology. In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions. The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments. The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity. Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization. Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

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 Massive MIMO for Future Wireless Communication Systems written by Agbotiname Lucky Imoize and published by John Wiley & Sons. This book was released on 2025-01-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authoritative resource discussing the development of advanced massive multiple input multiple output (MIMO) techniques and algorithms for application in 6G Massive MIMO for Future Wireless Communication Systems analyzes applications and technology trends for massive multiple input multiple output (MIMO) in 6G and beyond, presenting a unified theoretical framework for analyzing the fundamental limits of massive MIMO that considers several practical constraints. In addition, this book develops advanced signal-processing algorithms to enable massive MIMO applications in realistic environments. The book looks closer at applying techniques to massive MIMO in order to meet practical network constraints in 6G networks, such as interference, pathloss, delay, and traffic outage, and provides new insights into real-world deployment scenarios, applications, management, and associated benefits of robust, provably secure, and efficient security and privacy schemes for massive MIMO wireless communication networks. To aid in reader comprehension, this book includes a glossary of terms, resources for further reading via a detailed bibliography, and useful figures and summary tables throughout. With contributions from industry experts and researchers across the world and edited by two leaders in the field, Massive MIMO for Future Wireless Communication Systems includes information on: Signal processing algorithms for cell-free massive MIMO systems and advanced mathematical tools to analyze multiuser dynamics in wireless channels Bit error rate (BER) performance comparisons of different detectors in conventional cell-free massive MIMO systems Enhancement of massive MIMO using deep learning-based channel estimation and cell-free massive MIMO for wireless federated learning Low-complexity, self-organizing, and energy-efficient massive MIMO architectures, including the prospects and challenges of Terahertz MIMO systems Massive MIMO for Future Wireless Communication Systems is an essential resource on the subject for industry and academic researchers, advanced students, scientists, and engineers in the fields of MIMO, antennas, sensing and channel measurements, and modeling technologies.

Download or read book Coherent and Non coherent Data Detection Algorithms in Massive MIMO written by Haider Ali Jasim Alshamary and published by . This book was released on 2017 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side. In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the "temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector.