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

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

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 …

Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

Wireless sensor networks (WSN) have problems such as limited power, weak computing
power, poor communication ability, and vulnerability to attack. However, the existing …

Channel state information (CSI) estimation is one of the most fundamental problems in
wireless communication systems. Various methods, so far, have been developed to conduct …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …

Both caching and interference alignment (IA) are promising techniques for next-generation
wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless …

Edge networking is a complex and dynamic computing paradigm that aims to push cloud re-
sources closer to the end user improving responsiveness and reducing backhaul traffic …