Deep reinforcement learning based resource allocation for V2V communications

H Ye, GY Li, BHF Juang - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …

Distributed federated learning for ultra-reliable low-latency vehicular communications

S Samarakoon, M Bennis, W Saad… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-
latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide …

Deep-learning-based wireless resource allocation with application to vehicular networks

L Liang, H Ye, G Yu, GY Li - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

[PDF][PDF] Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review

JH Joloudari, R Alizadehsani, I Nodehi… - arXiv preprint arXiv …, 2022 - easychair.org
With the increasing growth of information through smart devices, increasing the quality level
of human life requires various computational paradigms presentation including the Internet …

A survey on radio resource allocation for V2X communication

A Masmoudi, K Mnif, F Zarai - Wireless Communications and …, 2019 - Wiley Online Library
Thanks to the deployment of new techniques to support high data rate, high reliability, and
QoS provision, Long‐Term Evolution (LTE) can be applied for diverse applications. Vehicle …

Toward intelligent vehicular networks: A machine learning framework

L Liang, H Ye, GY Li - IEEE Internet of Things Journal, 2018 - ieeexplore.ieee.org
As wireless networks evolve toward high mobility and providing better support for connected
vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular …

UAV-assisted vehicular edge computing for the 6G internet of vehicles: Architecture, intelligence, and challenges

J Hu, C Chen, L Cai, MR Khosravi… - IEEE Communications …, 2021 - ieeexplore.ieee.org
With the growing intelligence needed on the Internet of Vehicles (IoV), seamless edge
computing services for the sixth generation (6G) vehicle-to-everything (V2X) applications …

Federated learning for ultra-reliable low-latency V2V communications

S Samarakoon, M Bennis, W Saad… - 2018 IEEE global …, 2018 - ieeexplore.ieee.org
In this paper, a novel joint transmit power and resource allocation approach for enabling
ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The …

Toward 6G architecture for energy-efficient communication in IoT-enabled smart automation systems

AH Sodhro, S Pirbhulal, Z Luo… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Energy-efficient communication has become the center of attention from various
interdisciplinary fields, such as industrial automation, healthcare, and transportation, among …

Blockchain-based trust management model for location privacy preserving in VANET

B Li, R Liang, D Zhu, W Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Vehicular ad hoc network (VANET) is a special mobile ad hoc network (MANET) which plays
an important role in the intelligent traffic system (ITS). Based on the high mobility of VANETs …