Deep-Learning Based Proactive Handover for 5G/6G Mobile Networks using Wireless Information
Author:
Affiliation:
1. Samsung R & D Institute,Networks S/W R & D Group,Bengaluru,India
2. Samsung Electronics,Networks S/W R & D Group,Suwon,South Korea
3. Manchester Metropolitan University,Manchester,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10008390/10008474/10008702.pdf?arnumber=10008702
Reference28 articles.
1. Architecture Approaches for 5G Millimetre Wave Access Assisted by 5G Low-Band Using Multi-Connectivity
2. Dynamic Multi-Connectivity Performance in Ultra-Dense Urban mmWave Deployments
3. Multi-connectivity in 5G mmWave cellular networks
4. Architecture Approaches for 5G Millimetre Wave Access Assisted by 5G Low-Band Using Multi-Connectivity
5. Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks
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1. Multibeam antennas handover algorithm based on multi-objective Bayesian optimization;2024 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT);2024-05-31
2. Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity;Transactions on Emerging Telecommunications Technologies;2023-12-10
3. Blockage Prediction and Fast Handover of Base Station for Millimeter Wave Communications;IEEE Communications Letters;2023-08
4. Applications of Deep Learning and Deep Reinforcement Learning in 6G Networks;2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN);2023-07-04
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