Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

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Publication Details

Journal
IEEE Transactions on Wireless Communications
Published
2024-08-29
DOI
https://doi.org/10.1109/twc.2024.3447895
Citations
12
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
2.50

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article

Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems

Yan Yang, Rose Qingyang Hu, Miaowen Wen, Shuping Dang et al.
12 citations
IEEE Transactions on Wireless Communications
Millimeter-Wave Propagation and Modeling
2.50
article

Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems

Yan Yang, Rose Qingyang Hu, Miaowen Wen, Shuping Dang, Bo Ai
article en
12 citations

Abstract

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

IEEE Transactions on Wireless CommunicationsVol. 23(11)
Utah State University (US), Beijing Jiaotong University (CN), University of Bristol (GB), South China University of Technology (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 10%
Millimeter-Wave Propagation and Modeling
2.50
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