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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 26–50 of 52 papers

TitleStatusHype
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
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
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
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
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
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