공간다중화 MIMO 시스템에서 QRDM 알고리즘은 quasi-ML 성능을 가진다. 그러나 신호 검출과정에서 채널과 잡음 상황을 고려하지 않음으로써, 불필요한 연산을 지속할 뿐 아니라, 순차적인 검색 특성으로 인해 병렬 검색을 하지 못하는 한계점이 있다. 본 논문에서는 집합 그룹핑을 이용한 APQRDM 알고리즘과 AIQRDM 알고리즘을 제안한다. 제안된 검출 알고리즘은 집합 그룹핑을 이용하여 QRDM 알고리즘의 트리 검색 단계를 PDP(partial detection phases)로 분할하여 수행한다. 기존 QRDM 알고리즘의 트리 검색 단계가 4개의 PDP로 나누어질 때, APQRDM 알고리즘은 기존 QRDM 알고리즘의 1/4 에 해당하는 검출 지연을 가지며, AIQRDM 알고리즘은 기존 QRDM 알고리즘의 약 1/4에 해당하는 하드웨어 요구량을 가진다. 모의실험 결과는 4X4 시스템의 경우, APQRDM 알고리즘은 12dB의 Eb/N0에서 기존 QRDM 알고리즘의 약 43%에 해당하는 연산 복잡도를 가지며, AIQRDM 알고리즘은 0dB의 Eb/N0에서 기존 QRDM 알고리즘의 54%, AQRDM 알고리즘의 10%에 해당하는 연산 복잡도를 가짐을 보인다.
KSP Keywords
Quasi-ML
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