Programmable 28GHz mmWave MU-MIMO Testbed

Despite recent advancements in millimeter-wave (mm Wave) communication systems, the practical validation of research ideas in this field is still challenging. This is mostly due to the simplifying assumptions in their system modeling and overlooking hardware limitations in the simulation environment. In this paper, we propose a high-speed and programmable mm Wave testbed with multi-user (MU) multiple-input multiple-output (MIMO) beamforming capability. The baseband signal processing is preformed fully on a general-purpose processor. The radio frequency (RF) frontend holds a hybrid MIMO architecture, where each RF chain is connected to one phased-array antenna. The testbed allows to plug and play different physical layer signal processing algorithms or radio resource allocation techniques, including machine learning models, to assess their performance under real-life conditions.

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Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1109/wcnc61545.2025.10978370
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
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Programmable 28GHz mmWave MU-MIMO Testbed

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Advanced MIMO Systems Optimization
article

Programmable 28GHz mmWave MU-MIMO Testbed

Firooz B. Saghezchi, Baocang Ding, Haris Gacanin, Alexander Baron, Mohsen Pourghasemian, Ali Abdi, Amna Kopic, B.H. Lee
article en

Abstract

Despite recent advancements in millimeter-wave (mm Wave) communication systems, the practical validation of research ideas in this field is still challenging. This is mostly due to the simplifying assumptions in their system modeling and overlooking hardware limitations in the simulation environment. In this paper, we propose a high-speed and programmable mm Wave testbed with multi-user (MU) multiple-input multiple-output (MIMO) beamforming capability. The baseband signal processing is preformed fully on a general-purpose processor. The radio frequency (RF) frontend holds a hybrid MIMO architecture, where each RF chain is connected to one phased-array antenna. The testbed allows to plug and play different physical layer signal processing algorithms or radio resource allocation techniques, including machine learning models, to assess their performance under real-life conditions.

RWTH Aachen University (DE)
Affordable and clean energy
Openalex Percentile: Top 92%
Advanced MIMO Systems Optimization
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