Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) r...

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Bibliographic Details
Main Authors: Faris B. Mismar, Alperen Gundogan, Aliye Ozge Kaya, Oleg Chistyakov
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Beamforming
deep learning
handoff
predictive
radio resource management
transfer learning
Online Access:https://ieeexplore.ieee.org/document/10122528/

Internet

https://ieeexplore.ieee.org/document/10122528/

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