多入多出(MIMO)系统可以获得比单发单收系统更高的容量，对于MIMO系统，最大似然检测是最优接收，但其

指数复杂度难以在实际中应用。针对该问题，结合格缩减理论提出了基于Householder变换的复数域格缩减算法，将该算法和

MIMO次优检测算法相结合，给出了量化判决方法，且该复数域格缩减算法复杂度小于实数域格缩减算法。仿真结果表明，

基于Householder变换复数域格缩减的MIMO次优检测算法，通过优化信道矩阵可以得到更好的判决域，取得了逼近最优最大

似然检测算法的性能。-Multi-input multi-output (MIMO) systems can achieve more capacity than single input single

output systems. For MIMO systems, maximum likelihood detection is the optimum detection but its exponential

complexity limits its application. In this paper, a complex lattice reduction algorithm based on householder

transform is proposed and combined with MIMO suboptimal detection algorithm. Moreover the complexity of this

complex lattice reduction algorithm is lower than that of real lattice reduction algorithm. Simulation results show

that MIMO suboptimal detection algorithm based on this complex lattice reduction can approach the optimum

performance of maximum likelihood detection by optimizing channel matrix to get better decision domain.