MIMO系统中基于条件数阈值的格基约减信号检测算法

发布时间：2018-06-21 08:57

本文选题：多输入多输出 + 格基约减　；参考：《重庆邮电大学学报(自然科学版)》2017年06期

【摘要】：在多输入多输出系统中,最大似然(maximum likelihood,ML)检测算法性能最优但复杂度很高,最小均方误差(minimum mean-square error,MMSE)检测算法复杂度低但其性能较差。较高的信道矩阵条件数会给信号检测算法的误码率性能带来不利影响。针对这些问题,提出一种基于信道矩阵条件数阈值的信号检测算法来提升高条件数下传统检测算法的性能。该算法通过比较信道矩阵的条件数与预先设定的条件数阈值,选取相应的检测算法:当条件数低于阈值时,采用复杂度较低的LLL(lenstra-lenstra-lovasz)约减的MMSE(LLL-MMSE)算法来减少计算量;当条件数高于阈值时,采用基于排序分组的ML与LLL-MMSE联合的检测算法,通过增加一定的计算量来保证检测性能。对不同阈值下的误码率性能进行了仿真,结果表明算法的性能明显优于传统的LLL-MMSE检测算法,并且通过预先设定条件数阈值可以使得算法在性能和复杂度之间获得较好的平衡,最终达到优化检测算法性能的目的。
[Abstract]:In multi-input and multi-output systems, the maximum likelihood maximum (MLS) detection algorithm has the best performance but high complexity, and the minimum mean square error (MMSE) algorithm is low in complexity but poor in performance. The high number of channel matrix conditions will adversely affect the BER performance of the signal detection algorithm. To solve these problems, a signal detection algorithm based on the threshold of channel matrix condition number is proposed to improve the performance of traditional detection algorithm under high condition number. By comparing the condition number of the channel matrix with the pre-set threshold of the condition number, the algorithm selects the corresponding detection algorithm: when the condition number is lower than the threshold, the MMSE LLL-MMSE algorithm with lower complexity is used to reduce the computational complexity. When the number of conditions is higher than the threshold the detection algorithm of ML and LLL-MMSE based on sorting grouping is used to ensure the detection performance by adding a certain amount of computation. The BER performance at different thresholds is simulated. The results show that the performance of the algorithm is obviously superior to that of the traditional LLL-MMSE detection algorithm, and a better balance between performance and complexity can be obtained by pre-setting the threshold of the condition number. Finally, the performance of the detection algorithm is optimized.
【作者单位】：重庆邮电大学移动通信技术重庆市重点实验室;重庆邮电大学光电工程学院;
【基金】：重庆市基础与前沿研究计划项目(CSTC2015icyj A40040)~~
【分类号】：TN919.3

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