GfcLLL：一种基于贪婪选择的固定复杂度LLL约简方法
GfcLLL: A Greedy Selection Based Approach for Fixed-Complexity LLL Reduction
摘要：
LLL格型约简已被广泛用于降低Babai点的比特出错率，但其运行时间因矩阵而异。为了解决这个问题，已经提出了一些固定复杂度的LLL缩减（FCLLL）。本文提出了两种基于贪婪选择的FCLLL算法：GfcLLL（1）和GfcLLL（2）。仿真结果表明，这两种算法都能在相同或更短的CPU时间内得到误码率更低的Babai点。
英文摘要：
The LLL lattice reduction has been widely used to decrease the bit error rate (BER) of the Babai point, but its running time varies much from matrix to matrix. To address this problem, some fixed-complexity LLL reductions (FCLLL) have been proposed. In this paper, we propose two greedy selection based FCLLL algorithms: GfcLLL(1) and GfcLLL(2). Simulations show that both of them give Babai points with lower BER in similar or much shorter CPU time than existing ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
