.png){kind=icon}
返回
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
.png){kind=icon}
OA
AI
DOI：10.1109/TVT.2020.3047149.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the real-world environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the generalization error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.
Keyword:
Training
Wireless communication
Wireless sensor networks
Task analysis
Resource management
Upper bound
Sensors
Edge computing
machine learning
robotic communication
time division multiple access
vehicular communication
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
暂无机构信息
引用论文
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing边缘智能: 用边缘计算铺平人工智能的最后一英里.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading智能边缘: 利用深度模仿学习实现移动边缘计算卸载OAAI
OAAI分享收藏分享收藏
查看更多内容