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Lattice reduction and list based low complexity MIMO detection and its applications. / Lin Bai

Swansea University Author: Lin Bai

Abstract

Multiple input multiple output (MIMO) is an important technique of improving the spectral efficiency in wireless communications. In MIMO systems, it is usually required to jointly detect signals at the receiver. While the maximum likelihood (ML) MIMO detection provides an optimal performance with fu...

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Published: 2010
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42763
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last_indexed 2018-08-03T10:11:02Z
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spelling 2018-08-02T16:24:30.3985985 v2 42763 2018-08-02 Lattice reduction and list based low complexity MIMO detection and its applications. 46f5bbde0d892008143749574881d803 NULL Lin Bai Lin Bai true true 2018-08-02 Multiple input multiple output (MIMO) is an important technique of improving the spectral efficiency in wireless communications. In MIMO systems, it is usually required to jointly detect signals at the receiver. While the maximum likelihood (ML) MIMO detection provides an optimal performance with full receive diversity, its complexity grows exponentially with the number of transmit antennas. Thus, lattice reduction (LR) and list based detectors are developed to reduce the complexity. In this thesis, we first apply the partial maximum a posteriori probability (PMAP) principle to the list-based method for MIMO detection. It shows that the PMAP-based list detection outperforms the conventional list detection with a reasonably low complexity. To further improve the performance for slow fading MIMO channels, we develop the column reordering criteria (CRC) for the LR-based list detection. It shows that with our proposed CRC, the LR,-based list detection can provide a near ML performance with a sufficiently low complexity. Then, we develop a complexity efficient pre-voting cancellation based detection with pre-voting vector selection criteria for underdetermined MIMO systems and show that this scheme can exploit a near ML performance with full receive diversity. An extension of MIMO systems is multiuser MIMO systems, where the user selection becomes an effective way to increase diversity (multiuser diversity). If multiple users are selected to access the channel at a time, the selection problem becomes a combinatorial problem, where an exhaustive search may leads to highly computational complexity. Therefore, we propose a low complexity greedy user selection scheme with an iterative LR updating algorithm when a LR-based MIMO detector is used. It shows that the proposed selection scheme can provide a comparable performance to the combinatorial ones with much lower complexity. E-Thesis Electrical engineering. 31 12 2010 2010-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:30.3985985 2018-08-02T16:24:30.3985985 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Lin Bai NULL 1 0042763-02082018162520.pdf 10807532.pdf 2018-08-02T16:25:20.0530000 Output 4283262 application/pdf E-Thesis true 2018-08-02T16:25:20.0530000 false
title Lattice reduction and list based low complexity MIMO detection and its applications.
spellingShingle Lattice reduction and list based low complexity MIMO detection and its applications.
Lin Bai
title_short Lattice reduction and list based low complexity MIMO detection and its applications.
title_full Lattice reduction and list based low complexity MIMO detection and its applications.
title_fullStr Lattice reduction and list based low complexity MIMO detection and its applications.
title_full_unstemmed Lattice reduction and list based low complexity MIMO detection and its applications.
title_sort Lattice reduction and list based low complexity MIMO detection and its applications.
author_id_str_mv 46f5bbde0d892008143749574881d803
author_id_fullname_str_mv 46f5bbde0d892008143749574881d803_***_Lin Bai
author Lin Bai
author2 Lin Bai
format E-Thesis
publishDate 2010
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Multiple input multiple output (MIMO) is an important technique of improving the spectral efficiency in wireless communications. In MIMO systems, it is usually required to jointly detect signals at the receiver. While the maximum likelihood (ML) MIMO detection provides an optimal performance with full receive diversity, its complexity grows exponentially with the number of transmit antennas. Thus, lattice reduction (LR) and list based detectors are developed to reduce the complexity. In this thesis, we first apply the partial maximum a posteriori probability (PMAP) principle to the list-based method for MIMO detection. It shows that the PMAP-based list detection outperforms the conventional list detection with a reasonably low complexity. To further improve the performance for slow fading MIMO channels, we develop the column reordering criteria (CRC) for the LR-based list detection. It shows that with our proposed CRC, the LR,-based list detection can provide a near ML performance with a sufficiently low complexity. Then, we develop a complexity efficient pre-voting cancellation based detection with pre-voting vector selection criteria for underdetermined MIMO systems and show that this scheme can exploit a near ML performance with full receive diversity. An extension of MIMO systems is multiuser MIMO systems, where the user selection becomes an effective way to increase diversity (multiuser diversity). If multiple users are selected to access the channel at a time, the selection problem becomes a combinatorial problem, where an exhaustive search may leads to highly computational complexity. Therefore, we propose a low complexity greedy user selection scheme with an iterative LR updating algorithm when a LR-based MIMO detector is used. It shows that the proposed selection scheme can provide a comparable performance to the combinatorial ones with much lower complexity.
published_date 2010-12-31T03:53:36Z
_version_ 1763752664293703680
score 11.012678

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