随着人们对移动业务需求的迅猛发展和时频资源的日益紧缺，移动通信工程师们开始把目光投向了空间。正是如此，MIMO技术应运而生。MIMO技术带来了高的频谱利用率的同时也对终端的检测技术带来了挑战。考虑到手持终端的一些特点，比如：低功耗，小体积等。这就要求终端的MIMO检测算法必须在保证LTE系统要求的基本性能下，具有尽可能低的复杂度。 最佳的MIMO检测算法是最大似然检测，但是该算法的复杂度是一个NP问题，无法应用到工程实际中去。QR分解的ML检测算法是一个树搜索问题，因此降低复杂度的ML检测算法可以分成：深度优先的搜索算法和广度优先的搜索算法。深度优先的搜索算法包括了球形译码算法，VB算法，VB-SE算法。广度优先的搜索算法包括了M算法，排序的M算法。K-Best算法结合了二者优点，可以在性能和复杂度之间取得折衷。这些算法都属于非线性算法，其复杂度对于终端而言显得过大，因此基于QR分解的线性检测算法是研究重点。ZF算法虽然复杂度低，但是其性能也较差。MMSE算法性能较好，但是复杂度也随之升高。Wubben通过比较ZF算法和MMSE算法，提出了ZF类似的MMSE算法。但该算法的性能相比较MMSE算法下降较明显。基于启发式QR分解的ZF算法和ZF类似的MMSE算法性能较之ZF算法和ZF类似的MMSE算法有较大提升。 文章最后提出了一种新的算法IMRC。该算法结合了干扰抑制合并（IRC）和最大比合并（MRC）的思想。在第一层数据检测时，将第二层数据看成干扰信号，采用IRC是最佳接收。通过干扰消除，那么对于第二层数据只受到噪声的影响，采用MRC就是最佳接收。基于排序的之后，该算法性能还可以获得提升。

With the rapid development of mobile service demand and time-frequency resources increasingly scarce, mobile communications engineers began to turn their attention to space. For this case, MIMO technology came into being. MIMO technology has brought high spectral efficiency but also the terminal of the detection technology challenges. Take into account some of the features of the handheld terminal, such as: low power, small volume. MIMO detection algorithm which the terminal requires ensure that the requirements of the LTE system performance, with the lowest possible complexity. The best MIMO detection algorithm is the maximum likelihood detection, but the complexity of the algorithm is a NP problem. It can not be applied to engineering practice. QR decomposition of ML detection algorithm is a tree search problem, thus reducing the complexity of ML detection algorithm can be divided into: a depth-first search algorithm and breadth-first search algorithm. The depth-first search algorithm, include the sphere decoding algorithm, the VB algorithm, VB-SE algorithm. Breadth-first search algorithm, include the M algorithm, sorted the M algorithm. The K-Best algorithm combines both the advantages of sphere decoding algorithm and M algorithm; it is a trade-off between performance and complexity. These algorithms are nonlinear. These’ complexity is too large for the terminal, so linear detection algorithm based on QR decomposition is the research’s focus. ZF algorithm has low complexity, but its performance is poor. MMSE algorithm performance is better, but the complexity will be increased. Wubben compare ZF algorithm and the MMSE algorithm and presented the ZF-MMSE algorithm. However, the performance of this algorithm compared to MMSE algorithm decreased more apparent. ZF algorithm and the ZF-MMSE algorithm based on heuristic QR decomposition performance compared to the ZF algorithm and ZF-MMSE algorithm much improvement. The article concludes with a new algorithm 'IMRC'. The algorithm combines the idea of the Interference Restraint Combing(IRC) and Max Ratio Combing(MRC). In the first layer of data detection, the second layer of data can be seen an interference signal, the IRC is the best receiver. Interference cancellation, then the second layer of data affected only by noise, the MRC is the best receiver. After sorting, the algorithm performance can be improved.