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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 11–20 of 52 papers

TitleStatusHype
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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