Blind Equalization을 위한 Run and Go알고리즘
Run and Go Algorithm for Blind Equalization
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초록
이 논문은 high order statistics 에 기반을 둔 자력복구 알고리듬과 판정-LMS 를 조합하는 자력복구 적응 채널등화기를 제안한다. 저 신뢰도 신호에 대하여 적응을 멈추는 기존의 “Stop-and-Go" 알고리듬과는 달리 제안된 알고리듬은 저 신뢰도 신호에 대하여 자력복구 알고리듬 적응을 이용하기에 ”Run-and-Go" 라고 명명하였으며 판정-LMS의 최소 MSE 성능과 자력복구 알고리듬의 신호 획득능력의 시너지 효과를 가진다. 제안된 알고리듬은 신뢰도 구간을 신호의 질에 따라 자동설정 함으로 수렴 속도와 신호 획득 능력을 각각 개선한다.
Abstract
In this paper, we propose an adaptation strategy for blind equalizers, which combines a blind algorithm based on high order statistics and the decision directed LMS algorithm. In contrast to "Stop-and-Go" algorithm, where adaptation is stopped for unreliable signals, the proposed algorithm applies high order statistics (HOS) blind algorithm to the unreliable signals and applies DD-LMS for the reliable signals. The proposed algorithm, named "Run-and-Go" algorithm, inherits minimum MSE performance of DD-LMS and acquisition ability of blind algorithms. Furthermore, by updating the reliable signal region according to signal quality in each iteration, the convergence speed and acquisition ability is further improved.
참고문헌 (8)
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