The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Low-Complexity Soft-MIMO Detection Algorithm Based on Ordered Parallel Tree-Search Using Efficient Node Insertion

효율적인 노드 삽입을 이용한 순서화된 병렬 트리-탐색 기반 저복잡도 연판정 다중 안테나 검출 알고리즘

  • 김길환 (연세대학교 전기전자공학과 IT-SoC 연구실) ;
  • 박장용 (연세대학교 전기전자공학과 IT-SoC 연구실) ;
  • 김재석 (연세대학교 전기전자공학과 IT-SoC 연구실)
  • Received : 2012.07.23
  • Accepted : 2012.09.17
  • Published : 2012.10.30
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Abstract

This paper proposes an low-complexity soft-output multiple-input multiple-output (soft-MIMO) detection algorithm for achieving soft-output maximum-likelihood (soft-ML) performance under max-log approximation. The proposed algorithm is based on a parallel tree-search (PTS) applying a channel ordering by a sorted-QR decomposition (SQRD) with altered sort order. The empty-set problem that can occur in calculation of log-likelihood ratio (LLR) for each bit is solved by inserting additional nodes at each search level. Since only the closest node is inserted among nodes with opposite bit value to a selected node, the proposed node insertion scheme is very efficient in the perspective of computational complexity. The computational complexity of the proposed algorithm is approximately 37-74% of that of existing algorithms, and from simulation results for a $4{\times}4$ system, the proposed algorithm shows a performance degradation of less than 0.1dB.

본 논문은 max-log 근사화 하에서 연판정 최대 우도 (soft-output maximum-likelihood, soft-ML) 성능을 달성하기 위한 저복잡도 연판정 다중 안테나 (soft-output multiple-input multiple-output, soft-MIMO) 검출 알고리즘을 제안한다. 제안된 알고리즘은 병렬 트리-탐색 (parallel tree-search, PTS)을 기반으로 하며, 정렬 순서를 변경한 정렬된 QR 분해 (sorted-QR decomposition, SQRD)를 채널 순서화를 위해 적용한다. 비트별 로그-우도비 (log-likelihood ratio, LLR)를 계산하는 과정에서 발생할 수 있는 공집합 문제 (empty-set problem)는 탐색 레벨별로 추가적인 노드들을 삽입함으로써 해결한다. 제안된 노드 삽입 기법에서는 선택된 노드와 반대 비트 값을 가지면서 가장 가까운 노드만 삽입되기 때문에, 연산 복잡도 측면에서 상당히 효율적이다. 제안된 알고리즘의 연산 복잡도는 기존 알고리즘 대비 약 37-74% 수준이며, $4{\times}4$ 시스템에 대한 시뮬레이션 결과, 제안된 알고리즘은 soft-ML와 비교하여 0.1 dB 미만의 성능 저하를 보였다.

Keywords

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