Lattice reduction is a popular preprocessing strategy in multiple-input multiple-output (MIMO) detection. In a quest for developing a low-complexity reduction algorithm for large-scale problems, this paper investigates a new framework called sequential reduction (SR), which aims to reduce the lengths of all basis vectors. The performance upper bounds of the strongest reduction in SR are given when the lattice dimension is no larger than 4. The proposed new framework enables the implementation of a hash-based low-complexity lattice reduction algorithm, which becomes especially tempting when applied to large-scale MIMO detection. Simulation results show that, compared to other reduction algorithms, the hash-based SR algorithm exhibits the lowest complexity while maintaining comparable error performance.

中文翻译：

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用于大规模 MIMO 检测的低复杂度格约化算法：顺序约简的祝福

格子缩减是多输入多输出 (MIMO) 检测中流行的预处理策略。在寻求为大规模问题开发低复杂度约简算法的过程中，本文研究了一种称为顺序约简 (SR) 的新框架，旨在减少所有基向量的长度。当格维数不大于 4 时，给出了 SR 中最强约简的性能上限。 所提出的新框架能够实现基于哈希的低复杂度格约简算法，当应用于大尺度 MIMO 检测。仿真结果表明，与其他归约算法相比，基于哈希的 SR 算法在保持可比的错误性能的同时表现出最低的复杂度。