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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 11–20 of 52 papers

TitleStatusHype
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
A Low-Complexity Machine Learning Design for mmWave Beam Prediction—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
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction—0
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