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Beam Prediction

The beam prediction task involves determining the optimal beam or set of beams to use for signal transmission between a base station and a user device. Beam prediction is crucial in millimeter-wave (mmWave) and massive MIMO systems, where highly directional beams are used to overcome signal attenuation and ensure high data rates. The goal is to predict the most suitable beam(s) based on environmental factors, user location, and historical signal data, without exhaustive search over all possible beams, which can be computationally intensive.

Papers

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Showing 1–10 of 52 papers

TitleStatusHype
Multimodal Transformers for Wireless Communications: A Case Study in Beam PredictionCode1
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?Code1
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I CommunicationsCode1
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World DemonstrationCode1
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave DatasetsCode1
Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead—0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek—0
GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave CommunicationCode0
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