.png){kind=icon}
.png){kind=icon}
Return
Deep Learning-Assisted Energy-Efficient Task Offloading in Vehicular Edge Computing Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2021.3090179.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we study an energy-efficient computation offloading for vehicular edge computing systems, where multiple roadside units assist vehicular users to offload computation tasks to edge servers. Our goal is to minimize the users' energy consumption by optimizing user association, data partition, transmit power, and computation resources, subject to the constraints of partial tasks offloading, user latency, maximum transmit power, outage performance, and computation capacity of edge servers. We utilize deep learning for user association to avoid combinatorial complexity, and develop an efficient optimization algorithm to optimize other variables. The resulting algorithm has scalable complexity with convergence guarantee, as confirmed by our theoretical analysis. Simulation results demonstrate that the introduced resource allocation algorithm can significantly reduce the total energy consumption of users.
Keywords:
Servers
Task analysis
Fading channels
Energy consumption
Computational modeling
Resource management
Data models
Energy-efficient communications
computation offloading
vehicular communications
deep learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W