Deep deterministic policy gradient (DDPG)-based resource allocation scheme for NOMA vehicular communications
YH Xu, CC Yang, M Hua, W Zhou - IEEE Access, 2020 - ieeexplore.ieee.org
This paper investigates the resource allocation problem in vehicular communications based
on multi-agent Deep Deterministic Policy Gradient (DDPG), in which each Vehicle-to …
on multi-agent Deep Deterministic Policy Gradient (DDPG), in which each Vehicle-to …
Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks
In this paper, we study joint allocation of the spectrum, computing, and storing resources in a
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …
Intelligent resource management based on reinforcement learning for ultra-reliable and low-latency IoV communication networks
Internet of Vehicles (IoV) has attracted much interest recently due to its ubiquitous message
exchange and content sharing among smart vehicles with the development of the mobile …
exchange and content sharing among smart vehicles with the development of the mobile …
DRL‐based intelligent resource allocation for diverse QoS in 5G and toward 6G vehicular networks: a comprehensive survey
The vehicular network is taking great attention from both academia and industry to enable
the intelligent transportation system (ITS), autonomous driving, and smart cities. The system …
the intelligent transportation system (ITS), autonomous driving, and smart cities. The system …
Efficient power-splitting and resource allocation for cellular V2X communications
The research efforts on cellular vehicle-to-everything (V2X) communications are gaining
momentum with each passing year. It is considered as a paradigm-altering approach to …
momentum with each passing year. It is considered as a paradigm-altering approach to …
Distributed deep deterministic policy gradient for power allocation control in D2D-based V2V communications
Device-to-device (D2D) communication is an emerging technology in the evolution of the 5G
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …
Role of machine learning in resource allocation strategy over vehicular networks: a survey
I Nurcahyani, JW Lee - Sensors, 2021 - mdpi.com
The increasing demand for smart vehicles with many sensing capabilities will escalate data
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
Resource allocation for delay-sensitive vehicle-to-multi-edges (V2Es) communications in vehicular networks: A multi-agent deep reinforcement learning approach
The rapid development of internet of vehicles (IoV) has recently led to the emergence of
diverse intelligent vehicular applications such as automatic driving, auto navigation, and …
diverse intelligent vehicular applications such as automatic driving, auto navigation, and …
Meta-hierarchical reinforcement learning (MHRL)-based dynamic resource allocation for dynamic vehicular networks
With the rapid development of vehicular networks, there is an increasing demand for
extensive networking, computting, and caching resources. How to allocate multiple …
extensive networking, computting, and caching resources. How to allocate multiple …
Semantic communication-based dynamic resource allocation in d2d vehicular networks
The semantic communication mechanism enables wireless devices in vehicular networks to
communicate more effectively with the semantic meaning. However, in high-dynamic …
communicate more effectively with the semantic meaning. However, in high-dynamic …