Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach-Reference-Cited by-同舟云学术

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Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

Published:2023-10-10 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
language:
Short-container-title:

Author:

Ma Longfei¹,Cheng Nan¹,Wang Xiucheng¹,Yin Zhisheng²,Zhou Haibo³,Quan Wei⁴

Affiliation:

1. Xidian University,School of Telecommunications Engineering,Xi’an,China,710071

2. Xidian University,School of Cyber Engineering,Xi’an,China,710071

3. Nanjing University,School of Electronic Science and Engineering,Nanjing,China,210093

4. Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China,100044

Funder

Research and Development

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10333258/10333344/10333602.pdf?arnumber=10333602

Reference10 articles.

1. A Survey on Device-to-Device Communication in Cellular Networks

2. Learning to Optimize: Training Deep Neural Networks for Interference Management

3. Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

4. Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

5. Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

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