A survey on resource management in joint communication and computing-embedded SAGIN

Q Chen, Z Guo, W Meng, S Han, C Li… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-
terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As …

Network slicing: A next generation 5G perspective

P Subedi, A Alsadoon, PWC Prasad, S Rehman… - EURASIP Journal on …, 2021 - Springer
Abstract Fifth-generation (5G) wireless networks are projected to bring a major
transformation to the current fourth-generation network to support the billions of devices that …

Towards federated learning in UAV-enabled Internet of Vehicles: A multi-dimensional contract-matching approach

WYB Lim, J Huang, Z **ong, J Kang… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation
capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence …

Resource allocation based on deep reinforcement learning in IoT edge computing

X **ong, K Zheng, L Lei, L Hou - IEEE Journal on Selected …, 2020 - ieeexplore.ieee.org
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …

AI-assisted network-slicing based next-generation wireless networks

X Shen, J Gao, W Wu, K Lyu, M Li… - IEEE Open Journal …, 2020 - ieeexplore.ieee.org
The integration of communications with different scales, diverse radio access technologies,
and various network resources renders next-generation wireless networks (NGWNs) highly …

SDN/NFV-empowered future IoV with enhanced communication, computing, and caching

W Zhuang, Q Ye, F Lyu, N Cheng… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the
Internet with advanced communication and networking technologies, which can enhance …

NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of Things

L Qian, Y Wu, F Jiang, N Yu, W Lu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key
approaches for enabling computation-intensive yet delay-sensitive services in future …

A joint service migration and mobility optimization approach for vehicular edge computing

Q Yuan, J Li, H Zhou, T Lin, G Luo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The vehicular edge computing is considered an enabling technology for intelligent and
connected vehicles since the optimization of communication and computing on edge has a …

Delay-minimization routing for heterogeneous VANETs with machine learning based mobility prediction

Y Tang, N Cheng, W Wu, M Wang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Establishing and maintaining end-to-end connections in a vehicular ad hoc network
(VANET) is challenging due to the high vehicle mobility, dynamic inter-vehicle spacing, and …

Joint RAN slicing and computation offloading for autonomous vehicular networks: A learning-assisted hierarchical approach

Q Ye, W Shi, K Qu, H He, W Zhuang… - IEEE Open Journal of …, 2021 - ieeexplore.ieee.org
In this paper, a two-timescale radio access network (RAN) slicing and computing task
offloading problem is investigated for a cloud-enabled autonomous vehicular network (C …