Channel Estimation and Signal Detection with Model-driven Deep Learning for Massive Multiuser MIMO-OFDM Systems
Author: Changjiang Liu
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
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Author: Changjiang Liu
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKAuthor: A. Chockalingam
Publisher: Cambridge University Press
Published: 2014-02-06
Total Pages: 335
ISBN-13: 1107729254
DOWNLOAD EBOOKLarge MIMO systems, with tens to hundreds of antennas, are a promising emerging communication technology. This book provides a unique overview of this technology, covering the opportunities, engineering challenges, solutions and state of the art of large MIMO test beds. There is in-depth coverage of algorithms for large MIMO signal processing, based on meta-heuristics, belief propagation and Monte Carlo sampling techniques, and suited for large MIMO signal detection, precoding and LDPC code designs. The book also covers the training requirement and channel estimation approaches in large-scale point-to-point and multi-user MIMO systems; spatial modulation is also included. Issues like pilot contamination and base station cooperation in multi-cell operation are addressed. A detailed exposition of MIMO channel models, large MIMO channel sounding measurements in the past and present, and large MIMO test beds is also presented. An ideal resource for academic researchers, next generation wireless system designers and developers, and practitioners in wireless communications.
Author: Jianjun Ran
Publisher: Cuvillier Verlag
Published: 2008
Total Pages: 161
ISBN-13: 3867276498
DOWNLOAD EBOOKAuthor: Yonina C. Eldar
Publisher: Cambridge University Press
Published: 2022-08-04
Total Pages: 559
ISBN-13: 1108832989
DOWNLOAD EBOOKDiscover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.
Author: David Kin Wai Ho
Publisher:
Published: 2022
Total Pages: 158
ISBN-13:
DOWNLOAD EBOOKMassive 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.
Author: Shaodan Ma
Publisher: Open Dissertation Press
Published: 2017-01-27
Total Pages:
ISBN-13: 9781361469330
DOWNLOAD EBOOKThis dissertation, "Semi-blind Signal Detection for MIMO and MIMO-OFDM Systems" by Shaodan, Ma, 馬少丹, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "Semi-Blind Signal Detection for MIMO and MIMO-OFDM Systems" Submitted by Ma Shaodan for the degree of Doctor of Philosophy at The University of Hong Kong in May 2006 MIMO (Multiple Input Multiple Output) and MIMO-OFDM (Orthogonal Frequency Division Multiplexing) systems have attracted a lot of research interest in recent years due to their potential for future high speed wireless communications. This thesis focuses on the problem of signal detection and proposes three semi-blind algorithms for MIMO, MIMO-OFDM with short cyclic prefix (CP), and MIMO- OFDM without CP, respectively. A three-step semi-blind Rake-based multi-user detection technique is proposed for quasi-synchronous MIMO systems. The first step separates the multi-user multi-path signal vector into multi-user single-path signal vectors based on second-order statistics (SOS) of the received signals. A simple estimation method is proposed in the second step to estimate the time delays with the aid of pilots. The third step combines multiple multi-user single-path signal vectors for signal detection. System performance is improved by time diversity and only the upper bounds of the channel length and the time delays are required. Simulation results show that the proposed technique achieves good performance and is not sensitive to over-estimation of the maximum channel length and the maximum time delay. A MIMO-OFDM system with short CP is next considered for higher bandwidth efficiency and a time domain semi-blind signal detection algorithm is proposed. A new system model in which the i th received OFDM symbol is left shifted by J samples is introduced. Based on some structural properties of the new system model, an equalizer is designed using SOS of the received signals to cancel most of the inter- symbol interference (ISI). The transmitted signals are then detected from the equalizer output. In the proposed algorithm, only 2P ( P is the number of transmit antennas/users in MIMO-OFDM systems) columns of the channel matrix need to be estimated, and channel length estimation is unnecessary. In addition, the proposed algorithm is applicable irrespective of whether the channel length is shorter than, equal to or longer than the CP length. Simulation results verify the effectiveness of the proposed algorithm, and show that it outperforms the existing ones in all cases. Finally, in order to further improve bandwidth efficiency, a MIMO-OFDM system without CP is considered and a two-step semi-blind signal detection algorithm is proposed. The algorithm is based on some structural properties derived from shifting the received OFDM symbols. The first step cancels inter-carrier interference (ICI) and ISI with an equalizer designed using SOS of the shifted received OFDM symbols. The second step involves signal detection from the equalizer output in which the signals are still corrupted with multi-antenna interference (MAI). In the proposed algorithm, precise knowledge of the channel length is unnecessary and only one pilot OFDM symbol is utilized to estimate the required channel state information. Simulation results show that the proposed algorithm achieves comparable performance to algorithms for standard MIMO-OFDM systems and it is robust against channel length overestimation. The number of words: 460 DOI: 10.5353/th_b3684656 Subjects: Signal detection Algorithms MIMO systems Orthogonal
Author: Xin-She Yang
Publisher: Springer Nature
Published: 2021-10-26
Total Pages: 883
ISBN-13: 9811621020
DOWNLOAD EBOOKThis book gathers selected high-quality research papers presented at the Sixth International Congress on Information and Communication Technology, held at Brunel University, London, on February 25–26, 2021. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The book is presented in four volumes.
Author: Lin Bai
Publisher: Springer Science & Business Media
Published: 2012-01-08
Total Pages: 251
ISBN-13: 1441985832
DOWNLOAD EBOOKLow 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.
Author: Fabien Delestre
Publisher:
Published: 2011
Total Pages:
ISBN-13:
DOWNLOAD EBOOKThis thesis is concerned with channel estimation and data detection of MIMO-OFDM communication systems using Space-Time Block Coding (STBC) and Space-Frequency Block Coding (SFBC) under frequency selective channels. A new iterative joint channel estimation and signal detection technique for both STBC-OFDM and SFBC-OFDM systems is proposed. The proposed algorithm is based on a processive sequence of events for space time and space frequency coding schemes where pilot subcarriers are used for channel estimation in the first time instant, and then in the second time instant, the estimated channel is used to decode the data symbols in the adjacent data subcarriers. Once data symbols are recovered, the system recursively performs a new channel estimation using the decoded data symbols as pilots. The iterative process is repeated until all MIMO-OFDM symbols are recovered. In addition, the proposed channel estimation technique is based on the maximum likelihood (ML) approach which offers linearity and simplicity of implementation. Due to the orthogonality of STBC and SFBC, high computation efficiency is achieved since the method does not require any matrix inversion for estimation and detection at the receiver. Another major novel contribution of the thesis is the proposal of a new group decoding method that reduces the processing time significantly via the use of sub-carrier grouping for transmitted data recovery. The OFDM symbols are divided into groups to which a set of pilot subcarriers are assigned and used to initiate the channel estimation process. Designated data symbols contained within each group of the OFDM symbols are decoded simultaneously in order to improve the decoding duration. Finally, a new mixed STBC and SFBC channel estimation and data detection technique with a joint iterative scheme and a group decoding method is proposed. In this technique, STBC and SFBC are used for pilot and data subcarriers alternatively, forming the different combinations of STBC/SFBC and SFBC/STBC. All channel estimation and data detection methods for different MIMO-OFDM systems proposed in the thesis have been simulated extensively in many different scenarios and their performances have been verified fully.
Author: Kazuki Maruta
Publisher: MDPI
Published: 2020-07-03
Total Pages: 330
ISBN-13: 3039360167
DOWNLOAD EBOOKMultiple-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.