Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless …
This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, eg …
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 …
Objective To explore the application of deep neural networks (DNNs) and deep reinforcement learning (DRL) in wireless communication and accelerate the development of …
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent years for future wireless networks. They can overcome the ever-increasing challenges of …
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 …
Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a …
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) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic …