MU-MIMO Precoding Performance Conditioned by Inter-user Angular Separation

Trifan, Razvan-Florentin; Enescu, Andrei Alexandru

doi:10.1109/isetc.2018.8583919

Cited by 3 publications

(2 citation statements)

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“…In the MU-MIMO context, the user grouping is the task of forming a subset of users, according to a spatial compatibility metric. It can be shown that there is a correspondence between the precoding performance and the user grouping technique [9]. The scheduling of spatially-compatible users is crucial to suppress inter-user interference, whereas refining the power allocation and precoding weights requires less computational effort.…”

Section: User Groupingmentioning

confidence: 99%

“…Lastly, the employed intra-group precoder is based on linear regularized zero forcing [8]. While linear precoding mechanisms have a relatively low implementation complexity, they may not exhibit a good performance for highly-correlated channels, which is the case of users inside the same group [3,9]. Non-Linear Precoding (NLP) schemes, such as Tomlinson-Harashima Precoding (THP) [10], proposed in the standardization phase for 5G NR (New Radio) [11,12], or hybrid precoding [13], could behave better in such scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid MU-MIMO Precoding Based on K-Means User Clustering

2019

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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. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long-Term Evolution (LTE) networks; however, they do not work well for users with strongly-correlated channels. Alternatives to those schemes, like Non-Linear Precoding (NLP), and hybrid precoding schemes were proposed in the standardization phase for the Third-Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels and NLP schemes for users with strongly-correlated channels, can help reduce the computational burden, while limiting the performance degradation. Finding the optimum set of users that can be co-scheduled through LP schemes 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 user selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems employing hybrid precoding schemes.

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Section: User Groupingmentioning

confidence: 99%