Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

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 …

Recent reinforcement learning approaches for continuous control in wireless mobile
networks have shown impressive results. But due to the lack of open and compatible …

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

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

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 …

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

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" …

Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G)
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …

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