Hybrid Beamforming for Massive MIMO Over-the-Air Computation
Xiongfei Zhai, Xihan Chen, Jie Xu, Derrick Wing Kwan Ng

TL;DR
This paper proposes a hybrid beamforming approach for massive MIMO systems to improve over-the-air computation accuracy in IoT networks, addressing the challenge of vanishing SNR with large device numbers.
Contribution
It introduces a joint digital and hybrid beamforming optimization framework with efficient algorithms to minimize mean-squared error in AirComp systems.
Findings
Hybrid beamforming significantly improves computation accuracy.
MSE inversely proportional to number of antennas in fully-digital case.
Proposed algorithms outperform benchmark schemes.
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, as the number of devices becomes large, the computational accuracy of AirComp would seriously degrade due to the vanishing signal-to-noise ratio (SNR). 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…
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