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Beam Prediction
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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.
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M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G
Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek
GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
Illuminating the Path: Attention-Assisted Beamforming and Predictive Insights in 5G NR Systems
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks
Near-Field Beam Prediction Using Far-Field Codebooks in Ultra-Massive MIMO Systems
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks
STAR-RIS Aided Dynamic Scatterers Tracking for Integrated Sensing and Communications
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration
Multi-Modal Transformer and Reinforcement Learning-based Beam Management
Beam Prediction based on Large Language Models
Near-Field Sensing Enabled Predictive Beamforming: From Estimation to Tracking
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover
ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory
5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems
Model-based Deep Learning for Beam Prediction based on a Channel Chart
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach
A Low-Complexity Machine Learning Design for mmWave Beam Prediction
Uncertainty Aware Deep Learning for Particle Accelerators
Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios
Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave Systems
Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction
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