MMSE-GDFE lattice decoding for solving under-determined linear systems with integer unknowns

M. O. Damen, H. E. Gamal, G. Caire
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引用次数: 45

Abstract

Minimum mean square error generalized decision-feedback equalizer (MMSE-GDFE) lattice decoding is shown to be an efficient decoding strategy for under-determined linear channels. The proposed algorithm consists of an MMSE-GDFE front-end followed by a lattice reduction algorithm with a greedy ordering technique and, finally, a lattice search stage. By introducing flexibility in the termination strategy of the lattice search stage, we allow for trading performance for a reduction in the complexity. The proposed algorithm is shown, through experimental results in MIMO quasistatic channels, to offer significant gains over the state of the art decoding algorithms in terms of performance enhancement and complexity reduction. On the one hand, when the search is pursued until the best lattice point is found, the performance of the proposed algorithm is shown to be within a small fraction of a dB from the maximum likelihood (ML) decoder while offering a large reduction in complexity compared to the most efficient implementation of ML decoding proposed by Dayal and Varanasi (e.g., an order of magnitude in certain representative scenarios). On the other hand, when the search is terminated after the first point is found, the algorithm only requires linear complexity while offering significant performance gains (in the order of several dBs) over the linear complexity algorithm proposed recently by Yao and Wornell.
求解具有整数未知数的欠定线性系统的MMSE-GDFE点阵解码
最小均方误差广义决策反馈均衡器(MMSE-GDFE)点阵译码是一种有效的欠定线性信道译码策略。该算法由MMSE-GDFE前端、贪心排序的格约简算法和格搜索阶段组成。通过在晶格搜索阶段的终止策略中引入灵活性,我们允许交易性能降低复杂性。通过在MIMO准静态信道中的实验结果表明,所提出的算法在性能增强和降低复杂性方面比目前最先进的解码算法有显著的提高。一方面,当进行搜索直到找到最佳晶格点时,所提出算法的性能显示与最大似然(ML)解码器相差不到一个dB,同时与Dayal和Varanasi提出的最有效的ML解码实现相比(例如,在某些代表性场景中数量级)大大降低了复杂性。另一方面,当搜索在找到第一个点后终止时,该算法只需要线性复杂度,而与Yao和Wornell最近提出的线性复杂度算法相比,它提供了显著的性能提升(以几个db的顺序)。
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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