Research On LTE Terminal MIMO Estimate Algorithm

With the rapid development of mobile service demand and time-frequency resourcesincreasingly scarce, mobile communications engineers began to turn their attention to space.For this case, MIMO technology came into being. MIMO technology has brought highspectral efficiency but also the terminal of the detection technology challenges. Take intoaccount 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 theLTE system performance, with the lowest possible complexity.The best MIMO detection algorithm is the maximum likelihood detection, but thecomplexity 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 thecomplexity of ML detection algorithm can be divided into: a depth-first search algorithm andbreadth-first search algorithm. The depth-first search algorithm, include the sphere decodingalgorithm, the VB algorithm, VB-SE algorithm. Breadth-first search algorithm, include the Malgorithm, sorted the M algorithm. The K-Best algorithm combines both the advantages ofsphere decoding algorithm and M algorithm; it is a trade-off between performance andcomplexity. 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. ZFalgorithm has low complexity, but its performance is poor. MMSE algorithm performance isbetter, but the complexity will be increased. Wubben compare ZF algorithm and the MMSEalgorithm and presented the ZF-MMSE algorithm. However, the performance of thisalgorithm compared to MMSE algorithm decreased more apparent. ZF algorithm and theZF-MMSE algorithm based on heuristic QR decomposition performance compared to the ZFalgorithm and ZF-MMSE algorithm much improvement.The article concludes with a new algorithm ’IMRC’. The algorithm combines the idea ofthe 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 bestreceiver. Interference cancellation, then the second layer of data affected only by noise, theMRC is the best receiver. After sorting, the algorithm performance can be improved.