在大规模多输入多输出(MIMO)系统中，当用户数与基站(BS)天线数相当时，线性检测算法的性能很不理想。文章从非线性检测入手，提出一种格基约减(LR)辅助的贪心球形译码算法。通过格基约减算法辅助QR分解来获取更优的正交基并采用贪心策略改进球形译码算法，将每次欧式距离(PED)最小的符号作为根节点向下搜索，更高效地缩减了搜索树节点。理论分析及仿真结果表明，所提算法具有复杂度低且收敛速度快的特点，当系统误码率为10-4时，所提算法相较于线性算法有3~10 dB的性能提升。并且算法在一定程度上降低了近来所提出LR-K-Best算法的复杂度，具有较高的工程应用价值。

In the massive Multiple-Input Multiple-Output（MIMO） system, when the number of users is comparable (or equal) to the number of Base Station (BS) antennas, the performance of the linear detection algorithm is not ideal. We start with nonlinear detection and propose a greedy sphere decoding algorithm based on Lattice Reduction (LR). Lenstra-Lenstra-Lovasz algorithm assisted QR decomposition is proposed to obtain orthogonal bases, which then combines the improved sphere decoding algorithm with the greedy strategy. The search tree nodes are reduced more efficiently by searching the smallest Partial Euclidean Distance(PED) as the root node. Simulation results show that the proposed algorithm has the characteristics of low complexity and fast convergence. Compared with linear algorithms, the proposed algorithm has a 3~10 dB performance improvement when the system bit error rate is 10-4. Moreover, the algorithm reduces the complexity of the recently proposed LR-K-Best algorithm to a certain extent, and has high engineering application value.