A survey of blockchain and artificial intelligence for 6G wireless communications

Y Zuo, J Guo, N Gao, Y Zhu, S **… - … Surveys & Tutorials, 2023‏ - ieeexplore.ieee.org
The research on the sixth-generation (6G) wireless communications for the development of
future mobile communication networks has been officially launched around the world. 6G …

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 …

Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing

L Liu, J Feng, X Mu, Q Pei, D Lan… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …

Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks

L Liu, M Zhao, M Yu, MA Jan, D Lan… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to
provide low latency and reduce the load in backhaul networks. In order to meet drastically …

Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing

H Zhou, K Jiang, X Liu, X Li… - IEEE Internet of Things …, 2021‏ - ieeexplore.ieee.org
Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G
architecture, which can empower user equipments (UEs) with computation and energy …

Mobility-aware cooperative caching in vehicular edge computing based on asynchronous federated and deep reinforcement learning

Q Wu, Y Zhao, Q Fan, P Fan, J Wang… - IEEE Journal of …, 2022‏ - ieeexplore.ieee.org
Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular
users (VUs) in the roadside units (RSUs) to support real-time vehicular applications …

Convergence of edge computing and deep learning: A comprehensive survey

X Wang, Y Han, VCM Leung, D Niyato… - … surveys & tutorials, 2020‏ - ieeexplore.ieee.org
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art

QV Pham, F Fang, VN Ha, MJ Piran, M Le, LB Le… - IEEE …, 2020‏ - ieeexplore.ieee.org
Driven by the emergence of new compute-intensive applications and the vision of the
Internet of Things (IoT), it is foreseen that the emerging 5G network will face an …

Future intelligent and secure vehicular network toward 6G: Machine-learning approaches

F Tang, Y Kawamoto, N Kato, J Liu - Proceedings of the IEEE, 2019‏ - ieeexplore.ieee.org
As a powerful tool, the vehicular network has been built to connect human communication
and transportation around the world for many years to come. However, with the rapid growth …

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 …