LLL约简对Babai点成功概率和球形译码复杂度的影响
Effects of the LLL reduction on the success probability of the Babai point and on the complexity of sphere decoding
摘要：
在线性模型中估计未知整数参数向量的常用方法是求解整数最小二乘（ILS）问题。解决ILS问题的典型方法是球形解码。为了使球形解码器更快，通常使用众所周知的LLL减少作为预处理。由Babai最近计划算法产生的Babai点是ILS问题的次优解。首先证明了Babai点的成功概率作为ILS估计成功概率的下界比Hassibi和Boyd [1]给出的下界更尖锐。然后，我们严格地表明，应用LLL约简算法将增加Babai点的成功概率。最后，我们严格地表明，应用LLL减少算法也将减少球形解码器的计算复杂性，这是由文献中搜索树中的节点数近似测量的
英文摘要：
The common method to estimate an unknown integer parameter vector in a linear model is to solve an integer least squares (ILS) problem. A typical approach to solving an ILS problem is sphere decoding. To make a sphere decoder faster, the well-known LLL reduction is often used as preprocessing. The Babai point produced by the Babai nearest plan algorithm is a suboptimal solution of the ILS problem. First we prove that the success probability of the Babai point as a lower bound on the success probability of the ILS estimator is sharper than the lower bound given by Hassibi and Boyd [1]. Then we show rigorously that applying the LLL reduction algorithm will increase the success probability of the Babai point. Finally we show rigorously that applying the LLL reduction algorithm will also reduce the computational complexity of sphere decoders, which is measured approximately by the number of nodes in the search tree in the literature
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