The new generation mobile network requires flexibility and efficiency in radio link
management (RRM), in order to support a wide range of services and applications with …

In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …

It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

This article considers the problem of cost-aware downlink sum-rate maximization via joint
optimal radio access technologies (RATs) assignment and power allocation in next …

In future 5G systems, it is envisioned that the physical resources of a single network will be
dynamically shared between the virtual end-to-end networks called “slices” and the network …

The recent development of reinforcement learning (RL) has boosted the adoption of online
RL for wireless radio resource management (RRM). However, online RL algorithms require …

Deep learning has recently emerged as a disruptive technology to solve challenging radio
resource management problems in wireless networks. However, the neural network …

Centralized radio resource management method puts all of the computational burdens in an
agent, which is unbearable with the increasing of data dimensionality. This letter focuses on …

Recent work has demonstrated that cutting-edge advances in deep reinforcement learning
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …