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 …
A Feriani, E Hossain - IEEE Communications Surveys & …, 2021 - ieeexplore.ieee.org
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 …