Beamforming and Power Allocation for STAR-RIS-Aided mmWave Vehicular Communications with Coupled Phase Constraints

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhances millimeterwave (mmWave) communication by enabling full-space coverage, making it ideal for vehicle-to-everything (V2X) applications. This paper explores a multiuser multiple-input single-output (MU-MISO) mmWave non-orthogonal multiple access (NOMA) downlink vehicular environment aided by STAR-RIS, aiming to maximize the sum rate of the infrastructure-to-vehicle (I2V) links through the design of base station (BS) beamforming, power allocation, and STAR-RIS phase shifts under coupled phase constraints. We propose an unsupervised learning model for STAR-RIS phase design, complemented by analytical approaches for BS beamforming and power allocation. Simulations using the simulation of urban mobility (SUMO) software confirm the superior performance of the proposed scheme in various scenarios while meeting STAR-RIS and NOMA requirements.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/vtc2025-spring65109.2025.11174617
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
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article

Beamforming and Power Allocation for STAR-RIS-Aided mmWave Vehicular Communications with Coupled Phase Constraints

Wei‐Ho Chung, Ronald Y. Chang, Hongxin Chen, Hsin‐Yuan Chang
Millimeter-Wave Propagation and Modeling
article

Beamforming and Power Allocation for STAR-RIS-Aided mmWave Vehicular Communications with Coupled Phase Constraints

Wei‐Ho Chung, Ronald Y. Chang, Hongxin Chen, Hsin‐Yuan Chang
article en

Abstract

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhances millimeterwave (mmWave) communication by enabling full-space coverage, making it ideal for vehicle-to-everything (V2X) applications. This paper explores a multiuser multiple-input single-output (MU-MISO) mmWave non-orthogonal multiple access (NOMA) downlink vehicular environment aided by STAR-RIS, aiming to maximize the sum rate of the infrastructure-to-vehicle (I2V) links through the design of base station (BS) beamforming, power allocation, and STAR-RIS phase shifts under coupled phase constraints. We propose an unsupervised learning model for STAR-RIS phase design, complemented by analytical approaches for BS beamforming and power allocation. Simulations using the simulation of urban mobility (SUMO) software confirm the superior performance of the proposed scheme in various scenarios while meeting STAR-RIS and NOMA requirements.

National Tsing Hua University (TW), Research Center for Information Technology Innovation, Academia Sinica (TW)
Openalex Percentile: Top 73%
Millimeter-Wave Propagation and Modeling
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