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Cited by 74Advanced MIMO Systems OptimizationAdvanced Wireless Communication TechnologiesMillimeter-Wave Propagation and Modeling

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Hybrid Beamforming for Massive MIMO Over-the-Air Computation

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Zhai, Xiongfei, Chen, Xihan, Xu, Jie, Kwan Ng, Derrick Wing·2021·IEEE Transactions on Communications
DOI 10.1109/tcomm.2021.3051397
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Abstract

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, the computation accuracy of AirComp highly depends on the devices with the worst channels condition, which degrades severely when the number of devices becomes large. To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach, in which the transmit digital beamforming and the receive analog and digital beamforming are optimized in an alternating manner. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and coordinate descent (CD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.

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Metadata

Authors
Zhai, X., Chen, X., Xu, J., & Kwan Ng, D. W.
Affiliations
  • Guangdong University of Technology (CN)
  • Zhejiang University (CN)
  • Chinese University of Hong Kong, Shenzhen (CN)
ORCID
Venue
IEEE Transactions on Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-04
Volume
69
Issue
4
Pages
2737-2751
ISSN
  • 0090-6778 (ISSN-L)
  • 1558-0857
DOI
10.1109/tcomm.2021.3051397
OpenAlex
W3033723391
Cited by
74
References
3
Citations by year
2026: 6 · 2025: 13 · 2024: 14 · 2023: 23 · 2022: 12 · 2021: 4
FWCI
3.90
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Communications (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (U2001208, 61871137)
  • National Key Research and Development Program of China (2018YFB1800800)
  • Special Project for Research and Development in Key areas of Guangdong Province (2018B030338001)
  • Joint Funds of the National Natural Science Foundation of China and Guangdong (U2001203)
  • NSFC (61871136)
  • UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross-disciplinary fund scheme and by the Australian Research Council’s Discovery (DP210102169)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
Keywords
over-the-air computation, hybrid beamforming, massive MIMO, digital beamforming, analog beamforming, mean square error minimization, Internet of Things, data aggregation, successive convex approximation, coordinate descent, alternating optimization, transmit power constraint

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CoChat. (2026). Citation check: Hybrid Beamforming for Massive MIMO Over-the-Air Computation. https://app.cochat.ai/papers/10.1109/tcomm.2021.3051397
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Zhai, X., Chen, X., Xu, J., & Kwan Ng, D. W. (2021). Hybrid Beamforming for Massive MIMO Over-the-Air Computation. IEEE Transactions on Communications. https://doi.org/10.1109/tcomm.2021.3051397