arXiv (All)
open access
Cross-View Vision-Aided Proactive BS Selection and Beam Prediction for mmWave V2I Communications
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ENABLING MARITIME COMMAND AND CONTROL: EMPLOYING 5G FOR SENSING, TARGETING, AND DECISION SUPPORT AT SEA#657703
- Dataset for AI-Native Predictive Modeling of Dual-Band Wearable 5G/6G Antenna Systems#294287
- Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems#2601
- You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication#128430
- Physically Consistent Modeling of Dispersive Time-Modulated Reconfigurable Intelligent Surfaces for Wideband OFDM#1012548
Record · ID 820042
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive