Application of machine learning in wireless networks: Key techniques and open issues
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …
solving complex problems without explicit programming. Motivated by its successful …
Enabling AI in future wireless networks: A data life cycle perspective
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT)
networks, which can be mostly attributed to the increasing communication and sensing …
networks, which can be mostly attributed to the increasing communication and sensing …
Optimal Tethered-UAV Deployment in A2G Communication Networks: Multi-Agent Q-Learning Approach
An unmanned aerial vehicle-mounted base station (UAV-BS) is a promising technology for
the forthcoming sixth-generation wireless networks, owing to its flexibility and cost …
the forthcoming sixth-generation wireless networks, owing to its flexibility and cost …
Multiagent Q-Learning-Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design
In air-to-ground communications, the network lifetime depends on the operation time of
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity …
QoE-aware intelligent vertical handoff scheme over heterogeneous wireless access networks
J Chen, Y Wang, Y Li, E Wang - IEEE Access, 2018 - ieeexplore.ieee.org
As a measurement, quality of service (QoS) has been commonly taken into account in the
traditional vertical handoff schemes for the heterogeneous wireless access networks …
traditional vertical handoff schemes for the heterogeneous wireless access networks …
[HTML][HTML] Knowledge-defined networking: Applications, challenges and future work
Future 6G wireless communication systems are expected to feature intelligence and
automation. Knowledge-defined networking (KDN) is an evolutionary step toward …
automation. Knowledge-defined networking (KDN) is an evolutionary step toward …
A survey on requirements of future intelligent networks: solutions and future research directions
The context of this study examines the requirements of Future Intelligent Networks (FIN),
solutions, and current research directions through a survey technique. The background of …
solutions, and current research directions through a survey technique. The background of …
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 …
Dynamic spectrum allocation following machine learning-based traffic predictions in 5G
The popularity of mobile broadband connectivity continues to grow and thus, the future
wireless networks are expected to serve a very large number of users demanding a huge …
wireless networks are expected to serve a very large number of users demanding a huge …
[HTML][HTML] Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks
In cognitive radio (CR) ad-hoc network, the characteristics of the frequency resources that
vary with the time and geographical location need to be considered in order to efficiently use …
vary with the time and geographical location need to be considered in order to efficiently use …