Demonstrates advanced beamforming strategies enhance performance in mmWave V2I systems, indicating improved data transmission efficacy.
This paper proposes a novel beamforming weight design strategy for multiuser massive multiple-input multiple-output (MU-mMIMO) in millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) communication. The coordination of connected autonomous vehicles (CAVs) is expected to enhance route-planning efficiency and promote safe driving by sharing sensor data with roadside infrastructure and other vehicles. Given the extensive amount of sensor data, mmWave MU-mMIMO transmission is desirable. Although the mmWave MU-mMIMO system offers high-speed and high-capacity data transmission, it faces significant signal interference challenges owing to vehicle mobility. Conventional approaches, well-known as channel prediction-based null-steering, suffer from performance degradation under high mobility due to null misalignment. To address these challenges, this paper aims to develop a robust joint beamforming and null-steering strategy that maintains effective interference suppression while preserving desired signal gain in highly dynamic vehicular environments. Specifically, we use vehicle-side information, such as speed, angle, and position, and incorporate broad-range null-steering (BRNS) combined with beam tracking. BRNS utilizes the angles of interfering vehicles and steers additional nulls to suppress inter-user interference (IUI) in the MU-mMIMO system, while beam tracking compensates for the degradation in beamforming gain caused by DoF consumption. The effectiveness of the proposed weight design strategy is verified through computer simulations that incorporate realistic traffic scenarios.
No takes yet. Share an insight, caveat, or question.
Ozawa et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: