Implementation of quasi-maximum-likelihood detection based on semidefinite relaxation and linear programming

L. Rapoport, Zeng Yanxing, V. Ivanov, Shen Jianqiang
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Abstract

In this paper, a new numerical method is proposed for fast signal detection in large scale MIMO systems. Semidefinite relaxation (SDR) approach is utilized. The SDR problem is further reduced to the sequential linear programming by adding new form of cutting planes and column generation method. Bit error rate (BER) performance results conclude the paper. BER performance is compared with other MIMO detection algorithms. Performance of the new scheme practically identical to performance of the maximum-likelihood detection, while complexity is much less and does not depend on the conditioning number of the channel matrix.
基于半定松弛和线性规划的拟极大似然检测实现
本文提出了一种用于大规模MIMO系统快速信号检测的数值方法。采用半定松弛(SDR)方法。通过增加新的切割平面形式和列生成方法,将SDR问题进一步简化为序列线性规划。误码率(BER)性能结果是本文的结束语。将误码率性能与其他MIMO检测算法进行了比较。新方案的性能与最大似然检测的性能基本一致,但复杂度低得多,并且不依赖于信道矩阵的条件数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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