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K-Means MU-MIMO User Clustering for Optimized Precoding Performance

机译:K-Means MU-MIMO用户群集可优化预编码性能

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Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. Linear Precoding (LP) schemes for MU-MIMO are already used in Long Term Evolution (LTE) for their low complexity, however, they do not work well for users with strongly correlated channels. Finding the optimum set of co-scheduled users, provided their channels are separated enough, could require an exhaustive search, and thus may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal users selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems.
机译:在过去的几年中,从理论和实践的角度对多用户(MU)多输入多输出(MIMO)系统进行了广泛的研究。 MU-MIMO的线性预编码(LP)方案因其低复杂度而已在长期演进(LTE)中使用,但是,对于具有高度相关信道的用户而言,效果不佳。如果他们的频道足够分开,则要找到一组最佳的预定用户,这可能需要详尽的搜索,因此对于实际系统来说可能负担不起。本文的目的是提出一种基于统计K均值聚类的新型半正交用户选择算法,并评估其在MU-MIMO系统中的性能。

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