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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 1–25 of 52 papers

TitleStatusHype
Multimodal Transformers for Wireless Communications: A Case Study in Beam PredictionCode1
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?Code1
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World DemonstrationCode1
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I CommunicationsCode1
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave DatasetsCode1
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case—0
BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models—0
5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models—0
A Low-Complexity Machine Learning Design for mmWave Beam Prediction—0
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios—0
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information—0
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems—0
Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks—0
Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems—0
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction—0
Illuminating the Path: Attention-Assisted Beamforming and Predictive Insights in 5G NR Systems—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction—0
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