Low-Complexity Maximum-Likelihood Decoder for Four-Transmit- Antenna Quasi-Orthogonal Space–Time Block Code

Le, Minh-Tuan; Pham, Van-Su; Mai, Linh; Yoon, Giwan

doi:10.1109/tcomm.2005.858688

Cited by 31 publications

(18 citation statements)

References 8 publications

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“…A number of techniques were proposed in the literature to reduce the decoding complexity [22], [23]. In this paper, we show that this complexity can still be reduced substantially.…”

Section: Quasi-orthogonal Space-time Coding a System Model And mentioning

confidence: 73%

Reduction of ML decoding complexity for MIMO Sphere Decoding, QOSTBC, and OSTBC

Azzam

Ayanoğlu

2008

2008 Information Theory and Applications Workshop

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Abstract-In this paper, we discuss three applications of the QR decomposition algorithm to decoding in a number of MultiInput Multi-Output (MIMO) systems. In the first application, we propose a new structure for MIMO Sphere Decoding (SD). We show that the new approach achieves 80% reduction in the overall complexity compared to conventional SD for a 2 × 2 system, and almost 50% reduction for the 4 × 4 and 6 × 6 cases. In the second application, we propose a low complexity Maximum Likelihood Decoding (MLD) algorithm for quasiorthogonal space-time block codes (QOSTBCs). We show that for N = 8 transmit antennas and 16-QAM modulation scheme, the new approach achieves > 97% reduction in the overall complexity compared to conventional MLD, and > 89% reduction compared to the most competitive reported algorithms in the literature. This complexity gain becomes greater when the number of transmit antennas (N ) or the constellation size (L) becomes larger. In the third application, we propose a low complexity Maximum Likelihood Decoding (MLD) algorithm for orthogonal spacetime block codes (OSTBCs) based on the real-valued lattice representation and QR decomposition. For a system employing the well-known Alamouti OSTBC and 16-QAM modulation scheme, the new approach achieves > 87% reduction in the overall complexity compared to conventional MLD. Moreover, we show that for square L-QAM constellations, the proposed algorithm reduces the decoding computational complexity from O(L N/2 ) for conventional MLD to O(L) for systems employing QOSTBCs and from O(L) for conventional MLD to O( √ L) for those employing OSTBCs without sacrificing the performance.

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“…A number of techniques were proposed in the literature to reduce the decoding complexity [22], [23]. In this paper, we show that this complexity can still be reduced substantially.…”

Section: Quasi-orthogonal Space-time Coding a System Model And mentioning

confidence: 73%

Reduction of ML decoding complexity for MIMO Sphere Decoding, QOSTBC, and OSTBC

Azzam

Ayanoğlu

2008

2008 Information Theory and Applications Workshop

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“…In addition, ‖H(s − z)‖ 2 can be decomposed into two independent parts by utilizing (9), which results in…”

Section: Proposed ML Detectionmentioning

confidence: 99%

“…Compared against the sphere detection (SD) method, where the precomputation stage requires about 424 + 28 additions, 432 + 120 multiplications with real equivalent channel having 8 rows and 8 columns [17], or against the QR-based methods involving about 104 − 8 additions and 136 + 112 multiplications for the preparation stage [9], it is obvious that the overall complexity of the proposed model enjoys a clear advantage.…”

Section: Algorithm Initializationmentioning

confidence: 99%

“…This work also compares the performances of spatial multiplexing and QOSTBC with those of LRA zero-forcing and ML decoders. Authors of [9] employ QR decomposition and sorting to simplify detection of QOSTBC with four transmit antennas. This method achieves ML error performance and offers low complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the proposed method, Sphere [17] and QR [9] decoders are more complex because their search spaces cannot effectively be reduced, and they both require significant precomputation steps. As for the fast ML decoder in [10], authors utilize a simplified quadratic ML decoding statistic for QAM constellations, and comparison of the simulation results indicates that the average complexity of this work is higher than that of the proposed method confirming that the latter can be extended to any arbitrary constellations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Maximum-Likelihood Decoder for Quasi-Orthogonal Space-Time Block Code

Ahmadi

Talebi

2015

Mathematical Problems in Engineering

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Motivated by the decompositions of sphere and QR-based methods, in this paper we present an extremely fast maximum-likelihood (ML) detection approach for quasi-orthogonal space-time block code (QOSTBC). The proposed algorithm with a relatively simple design exploits structure of quadrature amplitude modulation (QAM) constellations to achieve its goal and can be extended to any arbitrary constellation. Our decoder utilizes a new decomposition technique for ML metric which divides the metric into independent positive parts and a positive interference part. Search spaces of symbols are substantially reduced by employing the independent parts and statistics of noise. Symbols within the search spaces are successively evaluated until the metric is minimized. Simulation results confirm that the proposed decoder's performance is superior to many of the recently published state-of-theart solutions in terms of complexity level. More specifically, it was possible to verify that application of the new algorithms with 1024-QAM would decrease the computational complexity compared to state-of-the-art solution with 16-QAM.

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Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

et al. 2018

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This paper presents a new decoder algorithm for the double space-time transmit diversity (DSTTD) system. The decoder is based on the QRD-M algorithm, which performs a breadth-first search of possible solutions tree. The search is simplified by skipping unlikely candidates, and it is stopped when no promising candidates are left. Furthermore, the search is divided into three concurrent iterations, making possible a fast, parallel implementation either in hardware or software. After presenting an analysis of the capacity and diversity of DSTTD, we present performance results showing that the proposed decoder is capable of achieving near maximum likelihood performance. We also show that the proposed algorithm exhibits lower computational complexity than other existing maximum likelihood detectors.

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Low-Complexity Maximum-Likelihood Decoder for Four-Transmit- Antenna Quasi-Orthogonal Space–Time Block Code

Cited by 31 publications

References 8 publications

Reduction of ML decoding complexity for MIMO Sphere Decoding, QOSTBC, and OSTBC

Reduction of ML decoding complexity for MIMO Sphere Decoding, QOSTBC, and OSTBC

Fast Maximum-Likelihood Decoder for Quasi-Orthogonal Space-Time Block Code

Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

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