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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
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World DemonstrationCode1
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I CommunicationsCode1
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?Code1
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave DatasetsCode1
Multimodal Transformers for Wireless Communications: A Case Study in Beam PredictionCode1
Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems—0
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models—0
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case—0
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios—0
A Low-Complexity Machine Learning Design for mmWave Beam Prediction—0
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