Spectrum allocation in the form of primary channel and bandwidth selection is a key factor
for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with …

Recent trends in deep reinforcement learning (DRL) have revealed the considerable
improvements to DRL algorithms in terms of performance, learning stability, and …

Next Generation (NextG) networks are expected to support demanding tactile internet
applications such as augmented reality and connected autonomous vehicles. Whereas …

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

Trends like softwarization through the usage of flexible Software-defined Radio (SDR)
platforms together with the usage of Machine Learning (ML) techniques are key enablers for …

Reinforcement Learning is gaining attention by the wireless networking community due to its
potential to learn good-performing configurations only from the observed results. In this work …

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency
communication. This architecture employs an open-loop transmission mode that prohibits all …

Abstract The effectiveness of Wi-Fi networks is greatly influenced by the optimization of
contention window (CW) parameters. Unfortunately, the conventional approach employed …

Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a
dramatic improvement in decision-making and automated control problems. As a result, we …

Recently, Deep Reinforcement Learning (DRL) has increasingly been used to solve
complex problems in mobile networks. There are two main types of DRL models: off-policy …