An improved LLR computation algorithm for QRM-MLD in coded MIMO systems오류정정부호가 적용된 MIMO 시스템에서 효율적인 LLR 계산 기법

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dc.contributor.advisorPark, Hyun-Cheol-
dc.contributor.advisor박현철-
dc.contributor.authorShin, Won-Jae-
dc.contributor.author신원재-
dc.date.accessioned2011-12-28T03:00:34Z-
dc.date.available2011-12-28T03:00:34Z-
dc.date.issued2007-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=392845&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/54865-
dc.description학위논문(석사) - 한국정보통신대학교 : 공학부, 2007.8, [ x, 54 p. ]-
dc.description.abstractFor next generation wireless communication systems, multiple-input and multiple-output (MIMO) systems have been receiving a great attention due to the fact that use of multiple transmit and receive antennas dramatically increases the capacity and diversity. To achieve optimal performance for the MIMO detection, the maximum likelihood (ML) detector which minimize joint error probability is necessary. However, the main 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. It becomes prohibitive when high order modulation is employed such as 16-QAM, 64-QAM and many of antennas are used. An ordered successive interference cancellation (OSIC) has been considered for implementation aspects. Although an OSIC detection scheme requires less computational complexity than ML detector, it undergos a significant performance degradation due to the error propagation. Several efforts have been focussed on achieving near-ML performance with low computation load recently. Among them, sphere decoding and maximum likelihood detection with QR-decomposition and $\It{M}$-algorithm (QRM-MLD) are most promising algorithms. Both algorithms are fascinating as they achieve near-ML and ML performance with only a small amount of the computational load. From the average complexity aspect, sphere decoding method has lower computational load than QRM-MLD algorithm. On the contrary, QRM-MLD algorithm has advantage over sphere decoding in implementation because its worst case complexity is much lower than that of sphere decoding. The QRM-MLD 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 QRM-MLD algorithm, $\It{M}$ must be larger than the constellation size. As the number of antennas and the size of modulation set are increased, a la...eng
dc.languageeng-
dc.publisher한국정보통신대학교-
dc.subjectQRM-MLD-
dc.subjectMaximum likelihood detection-
dc.subjectMIMO-
dc.subjectLLR computation-
dc.subjectLLR 계산 기법-
dc.subjectQR을 이용한 최대우도 검출기법-
dc.subject최대우도 검출기법-
dc.subject다입력 다출력 시스템-
dc.titleAn improved LLR computation algorithm for QRM-MLD in coded MIMO systems-
dc.title.alternative오류정정부호가 적용된 MIMO 시스템에서 효율적인 LLR 계산 기법-
dc.typeThesis(Master)-
dc.identifier.CNRN392845/225023-
dc.description.department한국정보통신대학교 : 공학부, -
dc.identifier.uid020054583-
dc.contributor.localauthorPark, Hyun-Cheol-
dc.contributor.localauthor박현철-
Appears in Collection
School of Engineering-Theses_Master(공학부 석사논문)
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