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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 1–10 of 52 papers

TitleStatusHype
Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead—0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek—0
GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave CommunicationCode0
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
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
Near-Field Beam Prediction Using Far-Field Codebooks in Ultra-Massive MIMO Systems—0
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