Digital Twin-Aided Vehicular Edge Network: A Large-Scale Model Optimization by Quantum-DRL
This paper presents an innovative large model framework for optimizing the task offloading
efficiency in vehicular edge networks, with a focus on ultra-reliable lowlatency …
efficiency in vehicular edge networks, with a focus on ultra-reliable lowlatency …
Digital Twin-assisted Space-Air-Ground Integrated Networks for Vehicular Edge Computing
In this paper, we present a framework that integrates digital twin (DT) technology into space-
air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our …
air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our …
Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks
Technological advancements of urban informatics and vehicular intelligence have enabled
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
Scheduling the operation of a connected vehicular network using deep reinforcement learning
Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network
(DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often …
(DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often …
[PDF][PDF] Decentralized vehicular edge computing framework for energy-efficient task coordination
Vehicular edge computing (VEC) empowers realtime applications in the autonomous
vehicle (AV) domain by positioning edge servers closer to AVs. This proximity reduces …
vehicle (AV) domain by positioning edge servers closer to AVs. This proximity reduces …
Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-based Deep Reinforcement Learning Approach
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
Collaborative data scheduling for vehicular edge computing via deep reinforcement learning
With the development of autonomous driving, the surging demand for data communications
as well as computation offloading from connected and automated vehicles can be expected …
as well as computation offloading from connected and automated vehicles can be expected …
An energy-efficient data offloading strategy for 5G-enabled vehicular edge computing networks using double deep Q-network
K Moghaddasi, S Rajabi… - Wireless Personal …, 2023 - Springer
In the era of fifth-generation (5G)-enabled vehicular edge computing (VEC), efficient data
offloading strategies are essential. The complexities inherent in this environment, such as …
offloading strategies are essential. The complexities inherent in this environment, such as …
Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing
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 …
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …