arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.02616cs.ITmath.IT
收藏

面向超大规模MIMO信号检测的群信息几何方法

Group Information Geometry Approach for Ultra-Massive MIMO Signal Detection

Jiyuan Yang, Yan Chen, Xiqi Gao, Xiang-Gen Xia, Dirk Slock

AI总结:

本文提出群信息几何方法(GIGA),通过分组构建流形并利用m-投影近似计算后验边缘概率,以低复杂度实现超大规模MIMO信号检测,仿真表明其在少量迭代内具有更优误码率性能。

摘要:

我们提出了一种用于超大规模多输入多输出(MIMO)信号检测的群信息几何方法(GIGA)。信号检测任务被构建为计算所有用户发送数据符号的后验分布的近似边缘概率。利用这些近似边缘概率,我们执行后验边缘概率最大化(MPM)检测来恢复每个用户的符号。基于信息几何理论和接收信号分量的分组,构建了三类流形,并通过m-投影获得近似后验边缘概率。引入Berry-Esseen定理来提供m-投影的近似计算,而其直接计算具有指数复杂度。在大多数情况下,分组越多,GIGA的复杂度越低。然而,当分组数量超过某一阈值时,GIGA的复杂度开始上升。仿真结果证实,所提出的GIGA在少量迭代内即可实现更优的误码率(BER)性能,这表明它可以作为超大规模MIMO系统中一种高效的检测方法。

英文摘要:

We propose a group information geometry approach (GIGA) for ultra-massive multiple-input multiple-output (MIMO) signal detection. The signal detection task is framed as computing the approximate marginals of the a posteriori distribution of the transmitted data symbols of all users. With the approximate marginals, we perform the maximization of the {\textsl{a posteriori}} marginals (MPM) detection to recover the symbol of each user. Based on the information geometry theory and the grouping of the components of the received signal, three types of manifolds are constructed and the approximate a posteriori marginals are obtained through m-projections. The Berry-Esseen theorem is introduced to offer an approximate calculation of the m-projection, while its direct calculation is exponentially complex. In most cases, more groups, less complexity of GIGA. However, when the number of groups exceeds a certain threshold, the complexity of GIGA starts to increase. Simulation results confirm that the proposed GIGA achieves better bit error rate (BER) performance within a small number of iterations, which demonstrates that it can serve as an efficient detection method in ultra-massive MIMO systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑