Beam Prediction
8 papers with code • 1 benchmarks • 1 datasets
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.
MVX
Most implemented papers
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
gourangc/Vision-Position-Beam-Prediction
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Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems.
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration
umut-demirhan/Radar-aided-beam-prediction
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This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications.
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
acyiobs/lidar_beam_tracking
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A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed.
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power.
Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
To address this challenge, we propose a novel direction that utilizes digital replicas of the physical world to reduce or even eliminate the MIMO channel acquisition overhead.
Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave Systems
BeechburgPieStar/Beam-Prediction
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IEEE Transactions on Vehicular Technology 2023
Specifically, data augmentation is implemented in the data pre-processing procedure with prior knowledge and then the signal splicing strategy is proposed in the training procedure.
Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
itu-ai-ml-in-5g-challenge/deepsense6g_tii
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Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS.
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.
Advancements in sixth-generation (6G) networks, coupled with the evolution of multimodal sensing in vehicle-to-everything (V2X) networks, have opened avenues for transformative research into multimodal-based artificial intelligence (AI) applications for wireless communication and network management.
