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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
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
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