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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 1–50 of 52 papers

TitleStatusHype
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I CommunicationsCode1
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave DatasetsCode1
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?Code1
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World DemonstrationCode1
Multimodal Transformers for Wireless Communications: A Case Study in Beam PredictionCode1
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models—0
Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems—0
Beam Prediction based on Large Language Models—0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios—0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information—0
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems—0
Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks—0
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction—0
Illuminating the Path: Attention-Assisted Beamforming and Predictive Insights in 5G NR Systems—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach—0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction—0
Model-based Deep Learning for Beam Prediction based on a Channel Chart—0
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization—0
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems—0
Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek—0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks—0
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond—0
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case—0
AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover—0
A Low-Complexity Machine Learning Design for mmWave Beam Prediction—0
STAR-RIS Aided Dynamic Scatterers Tracking for Integrated Sensing and Communications—0
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction—0
Uncertainty Aware Deep Learning for Particle Accelerators—0
Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration—0
Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning—0
5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models—0
Near-Field Beam Prediction Using Far-Field Codebooks in Ultra-Massive MIMO Systems—0
Near-Field Sensing Enabled Predictive Beamforming: From Estimation to Tracking—0
Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead—0
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework—0
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction—0
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.Code0
Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?Code0
Sensing-Aided 6G Drone Communications: Real-World Datasets and DemonstrationCode0
Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave SystemsCode0
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