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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
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
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?Code1
Multimodal Transformers for Wireless Communications: A Case Study in Beam PredictionCode1
Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave SystemsCode0
Sensing-Aided 6G Drone Communications: Real-World Datasets and DemonstrationCode0
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.Code0
GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave CommunicationCode0
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical NetworksCode0
Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?Code0
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
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems—0
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information—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
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks—0
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios—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
A Low-Complexity Machine Learning Design for mmWave Beam Prediction—0
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