Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial

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 …

Applications of deep reinforcement learning in communications and networking: A survey

NC Luong, DT Hoang, S Gong, D Niyato… - … surveys & tutorials, 2019 - ieeexplore.ieee.org
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

Deep reinforcement learning for Internet of Things: A comprehensive survey

W Chen, X Qiu, T Cai, HN Dai… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks

N Zhao, YC Liang, D Niyato, Y Pei… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …

Thirty years of machine learning: The road to Pareto-optimal wireless networks

J Wang, C Jiang, H Zhang, Y Ren… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
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 …

ColO-RAN: Developing machine learning-based xApps for open RAN closed-loop control on programmable experimental platforms

M Polese, L Bonati, S D'Oro, S Basagni… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Cellular networks are undergoing a radical transformation toward disaggregated, fully
virtualized, and programmable architectures with increasingly heterogeneous devices and …

Applications of multi-agent reinforcement learning in future internet: A comprehensive survey

T Li, K Zhu, NC Luong, D Niyato, Q Wu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …

Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications

RA Khalil, N Saeed, M Masood, YM Fard… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption

F Tang, B Mao, Y Kawamoto… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …

Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning

L Ale, N Zhang, X Fang, X Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Internet of Things (IoT) is considered as the enabling platform for a variety of promising
applications, such as smart transportation and smart city, where massive devices are …