Deep Reinforcement Learning for Wireless Communications and Networking
Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless …

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

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 chapter provides an overview of deep reinforcement learning (DRL) development in
wireless networks. In particular, we start with a brief overview of the development of wireless …

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

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

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

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

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 …

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