Compact Millimeter Wave Massive MIMO System Utilizing ESPAR

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications.

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Publication Details

Journal
IEEE Transactions on Communications
Published
2025-05-09
DOI
https://doi.org/10.1109/tcomm.2025.3568223
Citations
2
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
1.46
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article

Compact Millimeter Wave Massive MIMO System Utilizing ESPAR

Ross Murch, Dingfei Ma, Shanpu Shen, Chi Zhang et al.
2 citations
IEEE Transactions on Communications
Millimeter-Wave Propagation and Modeling
1.46
article

Compact Millimeter Wave Massive MIMO System Utilizing ESPAR

Ross Murch, Dingfei Ma, Shanpu Shen, Chi Zhang, Jun Qian, Bruno Clerckx, Zixiang Han, Hongyu Li
article en
2 citations

Abstract

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications.

IEEE Transactions on CommunicationsVol. 73(11)
Guangdong University of Technology (CN), University of Liverpool (GB), China Mobile (China) (CN), Hong Kong University of Science and Technology (HK), Peng Cheng Laboratory (CN), Imperial College London (GB), University of Hong Kong (HK)
Affordable and clean energy
Openalex Percentile: Top 18%
Millimeter-Wave Propagation and Modeling
1.46
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