Massive MIMO System Partitioning for Efficient Hybrid Beamformer Optimization

Hybrid analog-digital beamforming is an effective approach for practical implementations of a massive multiple-input multiple-output (MIMO) system by reducing the number of radio frequency (RF) chains. Fully connected hybrid beam-forming (F-HBF), where each RF chain is connected to each antenna, can lower hardware complexity, power consumption, and cost compared to digital beamforming. Subarray-based hybrid beamforming (S-HBF), where a specific group of RF chains is allocated to a particular subarray, can further reduce hardware requirements. The antenna array is divided into subarrays using effective partitioning so that the optimization of analog beamforming can be shared across multiple subarrays, substantially reducing computational complexity.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10887733
Primary Topic
Antenna Design and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Massive MIMO System Partitioning for Efficient Hybrid Beamformer Optimization

Saidur R. Pavel, Batu K. Chalise, Yimin D. Zhang
Antenna Design and Optimization
article

Massive MIMO System Partitioning for Efficient Hybrid Beamformer Optimization

Saidur R. Pavel, Batu K. Chalise, Yimin D. Zhang
article en

Abstract

Hybrid analog-digital beamforming is an effective approach for practical implementations of a massive multiple-input multiple-output (MIMO) system by reducing the number of radio frequency (RF) chains. Fully connected hybrid beam-forming (F-HBF), where each RF chain is connected to each antenna, can lower hardware complexity, power consumption, and cost compared to digital beamforming. Subarray-based hybrid beamforming (S-HBF), where a specific group of RF chains is allocated to a particular subarray, can further reduce hardware requirements. The antenna array is divided into subarrays using effective partitioning so that the optimization of analog beamforming can be shared across multiple subarrays, substantially reducing computational complexity.

New York Institute of Technology (US), Temple University (US)
National Science Foundation
Affordable and clean energy
Openalex Percentile: Top 94%
Antenna Design and Optimization
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