User Clustering and Hybrid Precoding Design for mm-Wave Multi-User Massive MIMO Systems

Interference cancellation in hybrid precoded downlink massive multi-user multi-input multi-output (MU-MIMO) systems, e.g., block diagonalization (BD), has a complexity that increases rapidly with the number of users. Spectral clustering into user groups based on mutual interference is proposed to reduce the complexity of servicing a large group of users in the same channel. By assigning users with the lowest mutual interference into separate groups, only intra-group interference cancellation needs to be considered. A relaxed suboptimal user grouping solution is proposed by characterizing MU-MIMO downlink interference using the pairwise angle between channel subspaces as graph weights for spectral clustering. Group assignment is achieved by discretization via constrained spectral clustering. The proposed approach, applied to mmWave multi-user massive MIMO systems, offers new system performance versus complexity trade-offs compared to conventional methods, as demonstrated by numerical results.

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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/pacrim61180.2024.10690191
Citations
1
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.21
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User Clustering and Hybrid Precoding Design for mm-Wave Multi-User Massive MIMO Systems

Majid H. Khoshafa, Steven D. Blostein
1 citations
Advanced MIMO Systems Optimization
0.21
article

User Clustering and Hybrid Precoding Design for mm-Wave Multi-User Massive MIMO Systems

Majid H. Khoshafa, Steven D. Blostein
article en
1 citations

Abstract

Interference cancellation in hybrid precoded downlink massive multi-user multi-input multi-output (MU-MIMO) systems, e.g., block diagonalization (BD), has a complexity that increases rapidly with the number of users. Spectral clustering into user groups based on mutual interference is proposed to reduce the complexity of servicing a large group of users in the same channel. By assigning users with the lowest mutual interference into separate groups, only intra-group interference cancellation needs to be considered. A relaxed suboptimal user grouping solution is proposed by characterizing MU-MIMO downlink interference using the pairwise angle between channel subspaces as graph weights for spectral clustering. Group assignment is achieved by discretization via constrained spectral clustering. The proposed approach, applied to mmWave multi-user massive MIMO systems, offers new system performance versus complexity trade-offs compared to conventional methods, as demonstrated by numerical results.

Queen's University (CA), McMaster University (CA)
Affordable and clean energy
Openalex Percentile: Top 48%
Advanced MIMO Systems Optimization
0.21
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