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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 1–25 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
Illuminating the Path: Attention-Assisted Beamforming and Predictive Insights in 5G NR Systems—0
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems—0
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework—0
Near-Field Beam Prediction Using Far-Field Codebooks in Ultra-Massive MIMO Systems—0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models—0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
STAR-RIS Aided Dynamic Scatterers Tracking for Integrated Sensing and Communications—0
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical NetworksCode0
Sensing-Aided 6G Drone Communications: Real-World Datasets and DemonstrationCode0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.Code0
Beam Prediction based on Large Language Models—0
Near-Field Sensing Enabled Predictive Beamforming: From Estimation to Tracking—0
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