Enhanced QRD-M algorithms using decision feedback detection for MIMO systems = MIMO 시스템에서 다중 decision feedback 수신기를 이용한 효율적 QRD-M 기법

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Multiple-input and multiple-output (MIMO) systems have been receiving a special attention as a promising candidate for next-generation communication systems due to the fact that use of multiple transmit and receive antennas dramatically increases the system capacity and diversity. The optimal detector of MIMO system is the maximum likelihood (ML) detector. However, the major problem with ML detector is its computational complexity. The complexity of ML detector increases exponentially according to the number of transmit antennas and the size of modulation set. For practical implementation, an ordered successive interference cancellation (OSIC) has been considered. Although an OSIC detection scheme requires less computational complexity than ML decision rule, it suffers from a significant performance degradation. Recently, several algorithms achieving near-ML or ML performance have been proposed. The tree search based QRD-M algorithm and sphere decoding are the promising algorithms. Both algorithms are attracting a special attention as they achieve near-ML and ML performance while requiring substantially low complexity. Sphere decoding has lower complexity than QRD-M algorithm in aspect of the average complexity. However, QRD-M algorithm has advantage over sphere decoding in implementation because its worst case complexity is much lower than that of sphere decoding. The QRD-M algorithm reduces the complexity by selecting $It\{M}$ candidates with the smallest accumulated metrics at each level of the tree search. To accomplish near-ML performance for QRD-M algorithm, $It\{M}$ must be the size of modulation set. As the number of antennas and the size of modulation set are large, a larger value of $It\{M}$ is needed. In this case, it still requires high computational complexity. In this respect, we introduce the new approach which reduce the complexity of conventional QRD-M algorithm by using decision feedback (DF) detection. The proposed method is based on two s...
Park, Hyun-Cheolresearcher박현철researcher
한국정보통신대학교 : 공학부,
Issue Date
392777/225023 / 020054604

학위논문(석사) - 한국정보통신대학교 : 공학부, 2007.2, [ viii, 56 p. ]


Threshold value; QRD-M algorithm; Tree searching; Partial DF detections; 부분 궤환 검출; 임계값; QRD-M 알고리즘; 트리 검색

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School of Engineering-Theses_Master(공학부 석사논문)
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