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This research aims to enhance tracking in wireless 5G-beyond networks using advanced machine learning techniques.

April 26, 2026Open Access

Repository Copy of: On the Vision-Beam Aided Tracking for Wireless 5G-Beyond Networks Using Long Short-Term Memory with Soft Attention Mechanism

Key Points

This research aims to enhance tracking in wireless 5G-beyond networks using advanced machine learning techniques.
Utilization of long short-term memory (LSTM) algorithms in tracking processes.
Implementation of a soft attention mechanism to improve tracking accuracy in network environments.
Demonstrated improved tracking precision using LSTM compared to conventional methods.
Identified potential for soft attention mechanisms to refine data handling and processing in wireless networks.

Abstract

This record is a repository-preserved copy of an article originally published in The European Journal of Research and Development by Orclever Science it is not the version of record, and Zenodo is not the publisher of this work. Version of Record (primary publication): https://doi.org/10.56038/ejrnd.v2i2.95 Publisher: Orclever Science & Research Group. Journal: The European Journal of Research and Development. For citation, please use the Crossref DOI and the journal citation above — not the Zenodo DOI.

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