Enhancing mobility management in 5G networks using deep residual LSTM model-Reference-Cited by-同舟云学术

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Enhancing mobility management in 5G networks using deep residual LSTM model

Published:2024-11 Issue: Volume:165 Page:112103
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Baz Abdullah,Logeshwaran Jaganathan^ORCID,Natarajan Yuvaraj,Patel Shobhit K.^ORCID

Publisher

Elsevier BV

Reference38 articles.

1. Mobility management solutions for 5G networks: architecture and services;Akkari;Comput. Netw.,2020

2. Multi-criteria handover mobility management in 5G cellular network;Palas;Comput. Commun.,2021

3. Distributed mobility management solutions for next mobile network architectures;Cominardi;Comput. Netw.,2017

4. Performance optimization of energy-efficient solar absorbers for thermal energy harvesting in modern industrial environments using a solar deep learning model;Armghan;Heliyon,2024

5. Neighbor predictive adaptive handoff algorithm for improving mobility management in VANETs;AlFarraj;Comput. Netw.,2019

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