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Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
Deep learning for intelligent wireless networks: A comprehensive survey
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 …
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
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 …
complex compelling applications both in military and civilian fields, where the users are able …
Wireless network intelligence at the edge
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …
based machine learning (ML) have transformed every aspect of our lives from face …
Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks
This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …
power control in wireless networks. Existing techniques typically find near-optimal power …
A gentle introduction to reinforcement learning and its application in different fields
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 …
become one of the most important and useful technology. It is a learning method where a …
Deep multi-user reinforcement learning for distributed dynamic spectrum access
We consider the problem of dynamic spectrum access for network utility maximization in
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
Machine learning for wireless communications in the Internet of Things: A comprehensive survey
Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …
wireless communications than ever before. For this reason, techniques such as spectrum …