针对传统多输入多输出( MIMO) 系统检测算法先检测的子流分集度较低以及错误传播的问题，提出了一种改进的迭代降维并行检测算法． 该算法在每次迭代内对第 1 个子流遍历取值，其余子流采用排序连续干扰消除( OSIC) 算法进行检测，在每次迭代结束时仅输出分集度最高的首子流的估计值，在迭代间通过干扰消除降低待检测子流的维度．仿真结果表明: 该算法能以较低的复杂度代价获得逼近最大似然检测算法的差错概率性能; 在 4 ×4、QPSK 调制的 MIMO 系统中，相对于传统的 OSIC 算法，文中算法在误比特率为 10^-3 时获得了 9． 3dB 的增益．

An improved iterative dimensionality-reduction parallel detection algorithm is proposed to mitigate the effects of the low diversity gain of the first detective sub-streams and the error propagation in traditional Multiple Input Multiple Output (MIMO) detection algorithm.In each iteration of the algorithm, the first substream is found by exhaustive search while other sub-streams are detected in parallel through ordered successive interference cancellation (OSIC) , and only the estimates of the first sub-stream with the highest diversity order can be obtained at the end of each iteration.Furthermore, between two different iterations, interference cancellation is employed to reduce the dimension of sub-streams.Simulated results indicate that, only with marginal complexity cost, the proposed algorithm helps obtain BER (Bit Error Rate) performance approaching maximum likelihood detection algorithm.Particularly, in a 4×4 QPSK modulation MIMO system, the performance gain of the proposed algorithm over OSIC is 9.3dB at a BER of 10^-3.