Objective To explore the application of deep neural networks (DNNs) and deep
reinforcement learning (DRL) in wireless communication and accelerate the development of …

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

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) algorithms with experience replays have been used to
solve many sequential learning problems. However, in practice, DRL algorithms still suffer …

The rapid development of mobile communication services in recent years has resulted in a
scarcity of spectrum resources. This paper addresses the problem of multi-dimensional …

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 …

As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …

Spectrum scarcity is a major challenge in wireless communications systems requiring
efficient usage and utilization. Cognitive radio network (CRN) is found as a promising …

In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …