CAJ | 학술논문

软球形译码算法虽然能接近 ML(最大似然)算法的误码性能，但其计算复杂度很高。文章提出了一种降低计算复杂度的 SSD(软球形译码)算法，该算法在 QR(正交三角)分解算法的反向迭代上三角矩阵 R 中引入减弱噪声部分以缩小初始搜索半径，然后利用最小距离准则对搜索树进行有效地删减，缩小树搜索空间。MATLAB 仿真结果表明，该算法在获得接近传统SSD 性能的条件下，能够很大程度地降低系统的计算复杂度。
연구형역마산법수연능접근 ML(최대사연)산법적오마성능，단기계산복잡도흔고。문장제출료일충강저계산복잡도적 SSD(연구형역마)산법，해산법재 QR(정교삼각)분해산법적반향질대상삼각구진 R 중인입감약조성부분이축소초시수색반경，연후이용최소거리준칙대수색수진행유효지산감，축소수수색공간。MATLAB 방진결과표명，해산법재획득접근전통SSD 성능적조건하，능구흔대정도지강저계통적계산복잡도。
Although soft-output sphere decoding algorithm can approach to the bit error performance of Maximum Likelihood (ML)algorithm,it has high computational complexity.This paper presents a complexity-reduced soft-output sphere decoding algorithm,which introduces the reduced noise element to the reverse iterative upper triangular matrix R of QR decomposition algorithm so as to narrow the initial search radius.Then it uses the minimum distance criterion to effectively trim the search tree and narrow the tree search space.MATLAB simulation results show that this algorithm can reduce the system computa-tional complexity to a great extent while approaching to the bit error performance of traditional soft sphere decoding.