Machine learning-based load balancing algorithms in future heterogeneous networks: A survey
The massive growth of mobile users and the essential need for high communication service
quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) …
quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) …
A literature survey on AI-aided beamforming and beam management for 5G and 6G systems
DS Brilhante, JC Manjarres, R Moreira… - Sensors, 2023 - mdpi.com
Modern wireless communication systems rely heavily on multiple antennas and their
corresponding signal processing to achieve optimal performance. As 5G and 6G networks …
corresponding signal processing to achieve optimal performance. As 5G and 6G networks …
A survey of machine learning applications to handover management in 5G and beyond
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication
networks that need to be properly managed since it poses multiple threats to quality-of …
networks that need to be properly managed since it poses multiple threats to quality-of …
Connection management xAPP for O-RAN RIC: A graph neural network and reinforcement learning approach
Connection management is an important problem for any wireless network to ensure smooth
and well-balanced operation throughout. Traditional methods for connection management …
and well-balanced operation throughout. Traditional methods for connection management …
Multiobjective load balancing for multiband downlink cellular networks: A meta-reinforcement learning approach
Load balancing has become a key technique to handle the increasing traffic demand and
improve the user experience. It evenly distributes the traffic across network resources by …
improve the user experience. It evenly distributes the traffic across network resources by …
[HTML][HTML] A comparative study of machine learning-based load balancing in high-speed train system
With the rapid developments of fifth generation (5G) mobile communication networks in
recent years, different use cases can now significantly benefit from 5G networks. One such …
recent years, different use cases can now significantly benefit from 5G networks. One such …
Optimal resource allocation considering non-uniform spatial traffic distribution in ultra-dense networks: A multi-agent reinforcement learning approach
Recently, the demand for small cell base stations (SBSs) has been exploding to
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
Reinforcement learning for user association and handover in mmwave-enabled networks
A Alizadeh, M Vu - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
Using a multi-armed bandit technique, we propose centralized and semi-distributed online
algorithms for load balancing user association and handover in mmWave-enabled networks …
algorithms for load balancing user association and handover in mmWave-enabled networks …
Multi-agent Q-learning for real-time load balancing user association and handover in mobile networks
As next generation cellular networks become denser, associating users with the optimal
base stations at each time while ensuring no base station is overloaded becomes critical for …
base stations at each time while ensuring no base station is overloaded becomes critical for …
Machine learning-based solutions for handover decisions in non-terrestrial networks
MK Dahouda, S Jin, I Joe - Electronics, 2023 - mdpi.com
The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources
mounted on satellites and includes satellite-based communications networks, high altitude …
mounted on satellites and includes satellite-based communications networks, high altitude …