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

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 …

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 …

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

Internet of Drones (IoD) is one of the promising technologies to enhance the performance of
wireless networks. Deploying IoD to assist wireless networks, however, needs to address …