Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …

There is a phenomenal burst of research activities in machine learning and wireless
systems. Machine learning evolved from a collection of powerful techniques in AI areas and …

The rapid introduction of mobile devices and the growing popularity of mobile applications
and services create unprecedented infrastructure requirements for mobile and wireless …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the
wireless networks domain. They are considered promising approaches for solving dynamic …

Large bandwidth and more mobility are only two reasons why wireless and mobile networks
are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous …

Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …