Space-time Codes and MIMO Systems
Space-time Codes and MIMO Systems

Author: Mohinder Jankiraman

Publisher: Artech House

Published: 2004

Total Pages: 354

ISBN-13: 9781580538664

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Annotation "This resource takes professionals step by step from the basics of MIMO through various coding techniques, to critical topics such as multiplexing and packet transmission. Practical examples are emphasized and mathematics is kept to a minimum, so readers can quickly and thoroughly understand the essentials of MIMO. The book takes a systems view of MIMO technology that helps professionals analyze the benefits and drawbacks of any MIMO system."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved.

Channel Estimation and Performance Analysis of MIMO-OFDM Communications Using Space-time and Space-frequency Coding Schemes
Channel Estimation and Performance Analysis of MIMO-OFDM Communications Using Space-time and Space-frequency Coding Schemes

Author: Fabien Delestre

Publisher:

Published: 2011

Total Pages:

ISBN-13:

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This 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.

Space-Time Processing for MIMO Communications
Space-Time Processing for MIMO Communications

Author: Alex Gershman

Publisher: John Wiley & Sons

Published: 2005-08-05

Total Pages: 388

ISBN-13: 0470010037

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Driven by the desire to boost the quality of service of wireless systems closer to that afforded by wireline systems, space-time processing for multiple-input multiple-output (MIMO) wireless communications research has drawn remarkable interest in recent years. Exciting theoretical advances have been complemented by rapid transition of research results to industry products and services, thus creating a vibrant new area. Space-time processing is a broad area, owing in part to the underlying convergence of information theory, communications and signal processing research that brought it to fruition. This book presents a balanced and timely introduction to space-time processing for MIMO communications, including highlights of emerging trends, such as spatial multiplexing and joint transceiver optimization. Includes detailed coverage of wireless channel sounding, modelling, characterization and model validation. Provides state-of-the-art research results on space-time coding, including comprehensive tutorial coverage of orthogonal space-time block codes. Discusses important recent developments in spatial multiplexing, transmit beam-forming, pre-coding and joint transceiver design for the multi-user MIMO downlink using full or partial CSI. Illustrates all theory with numerous examples gleaned from cutting-edge research from around the globe. This valuable resource will appeal to engineers, developers and consultants involved in the design and implementation of space-time processing for MIMO communications. Its accessible format, amply illustrated with real world case studies, contains relevant, detailed advice for postgraduate students and researchers specializing in this field.

Space-Time Coding for Broadband Wireless Communications
Space-Time Coding for Broadband Wireless Communications

Author: Georgios B. Giannakis

Publisher: John Wiley & Sons

Published: 2007-02-26

Total Pages: 488

ISBN-13: 047146287X

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Eine vielversprechende Technologie zur Maximierung der Bandbreiteneffizienz in der breitbandigen drahtlosen Kommunikation ist die Raum-Zeit-Kodierung. Theorie und Praxis verbindend, ist dieses Buch die erste umfassende Diskussion von Grundlagen und designorientierten Aspekten von Raum-Zeit-Codes. Single-Carrier und Multi-Carrier-Übertragungen für Einzel- und Mehrnutzerkommunikation werden behandelt.

Layered Space-time Structure for MIMO-OFDM Systems
Layered Space-time Structure for MIMO-OFDM Systems

Author: Jianxuan Du

Publisher:

Published: 2005

Total Pages: 94

ISBN-13:

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The low complexity of layered processing makes the layered structure a promising candidate for MIMO systems with a large number of transmit antennas and higher order modulation. For broadband systems, orthogonal frequency division multiplexing (OFDM) appears promising for its immunity against delay spread. In addition, OFDM is especially suitable for frequency selective MIMO systems since the introduction of orthogonal subcarriers makes system design and implementation as simple as those for flat fading channels. Therefore, the combination of layered structure with OFDM is a promising technique for high-speed wireless data transmission. The proposed research is focused on the layered structure for MIMO-OFDM systems, where several techniques are proposed for performance enhancement, namely, channel estimation based on subspace tracking, parallel detection of group-wise space-time codes by predictive soft interference cancellation, quasi-block diagonal low-density parity-check codes (LDPC) coding and statistical data rate allocation for layered systems. For MIMO-OFDM systems, rank reduction by some linear transform matrix is necessary for channel estimation. In the proposed research, we propose a channel estimation algorithm for MIMO-OFDM systems, which uses the optimum low-rank channel approximation obtained by tracking the frequency autocorrelation matrix of the channel response. Then parallel detection algorithm is proposed for a modified layered system with group-wise space-time coding, where the structure of particular component space-time code trellises is exploited using partial information from the Viterbi decoder of the simultaneously decoded interfering component codes. Next we incorporate the layered structure with LDPC to develop a quasi-block diagonal LDPC space-time structure. The lower triangular structure of the parity check matrix introduces correlation between layers. Each layer, as a part of the whole codeword, can be decoded while taking information from other undetected layers to improve the decoding performance. In the end, a modified layered structure is proposed where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. With Gaussian approximation of layer capacities, we derive the optimum data rate allocation.