Near ML decoding based on metric-first searching and branch length threshold for multiple input multiple output systems = 여러 입력 여러 출력 시스템에서 길이 먼저 살펴보기와 가지 길이 문턱값을 바탕으로 한 준최적 복호

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In this thesis, we propose a near maximum likelihood (ML) scheme for the decoding of multiple input multiple output systems. Based on the metric-first search method and by employing Schnorr-Euchner enumeration and branch length thresholds, the proposed technique provides a higher efficiency than other conventional near ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has lower computational complexity than other near ML decoders while maintaining the bit error rate very close to the ML performance. The proposed scheme in addition possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.
Advisors
Song, Iick-horesearcher송익호researcher
Description
한국과학기술원 : 전기및전자공학전공,
Publisher
한국과학기술원
Issue Date
2008
Identifier
301990/325007  / 020063302
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학전공, 2008. 8., [ v, 44 p. ]

Keywords

multiple input multiple output system; tree search; metric-first search; Schnorr-Euchner enumeration; branch length threshold; 여러 입력 여러 출력 시스템; 나무 살펴보기; 길이 먼저 살펴보기; 슈노르-오히너 벌여 놓기; 가지 길이 문턱값; multiple input multiple output system; tree search; metric-first search; Schnorr-Euchner enumeration; branch length threshold; 여러 입력 여러 출력 시스템; 나무 살펴보기; 길이 먼저 살펴보기; 슈노르-오히너 벌여 놓기; 가지 길이 문턱값

URI
http://hdl.handle.net/10203/38649
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=301990&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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