This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I
Reliable receive-beam alignment at a ground base station (BS) is essential for uplink millimeter-wave (mmWave) communication from a moving uncrewed aerial vehicle (UAV), but existing conformal beam-selection methods largely treat successive decisions independently. We propose Sequential Conformal Prediction with Empiri...
Zahra Zarei, Chung G. Kang· IEEE Access· 0 citations
Dynamic blockage severely degrades vehicular integrated sensing and communication (ISAC), since narrow millimeter-wave beams must be adjusted before the line-of-sight (LoS) link is blocked or misaligned. This paper proposes WM4ISAC, a proactive ISAC framework built on a world model that learns a reusable latent dynamic...
Zi-Chao Xiao, Lin-Long Wu, R. BhavaniShankarMysore· 0 citations
Millimeter-wave communications are highly vulnerable to line-of-sight (LOS) blockage, which can cause severe link quality (LQ) degradation. To enable proactive control for stable mmWave communication, deep neural network (DNN)-based LQ prediction using physical space information—such as the user equipment (UE) position...
Millimeter-wave (mmWave) vehicular networks are a key enabler of beyond-5G intelligent transportation systems but are highly vulnerable to fast mobility, dynamic blockages, and rapid topology variations, which often cause beam misalignment and quality-of-service (QoS) degradation. Data-driven beamforming based on artif...
Millimeter-wave integrated sensing and communication (ISAC) roadside units need to steer narrow pencil beams toward fast-moving vehicles, and a short sensing outage can trigger a divergent tracking loop: As the tracker drifts, the beam widens, subsequent measurements degrade, and recovery becomes increasingly difficult...
Le-Heng Qi, Xiao-Yang Li, Yi Gong· IEEE International Symposium...· 0 citations
Millimeter-wave vehicle-to-everything (V2X) communications are a promising technology for supporting high-datarate cooperative sensing applications, such as LiDAR point-cloud sharing among vehicles and roadside infrastructure. However, mmWave links are highly sensitive to mobility-induced blockage, line-of-sight (LoS)...
Shintaro Habu, Roberto Pegurri, Kui Wang et al.· IEEE International Symposium...· 0 citations
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