Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks-Reference-Cited by-同舟云学术

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Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks

Published:2022-04-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Wireless Communications and Networking Conference (WCNC)
language:
Short-container-title:

Author:

Ahmadi Arian¹,Semiari Omid¹,Bennis Mehdi²,Debbah Merouane³

Affiliation:

1. University of Colorado,Department of Electrical and Computer Engineering,Colorado Springs,CO,USA,80918

2. University of Oulu,Department of Communications Engineering,Oulu,Finland,FI-90014

3. Mohamed Bin Zayed University of Artificial Intelligence,Technology Innovation Institute,Masdar City, Abu Dhabi,United Arab Emirates,9639

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9771381/9771540/09771710.pdf?arnumber=9771710

Reference16 articles.

1. Deep reinforcement learning based computation offloading and resource allocation for MEC

2. Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

3. Ultra Reliable, Low Latency Vehicle-to-Infrastructure Wireless Communications with Edge Computing

4. Auto-encoding variational bayes;kingma,2013

5. ?-vae: Autoregressive parametrization of the vae encoder;ferdowsi,2019

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1. At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence;IEEE Open Journal of the Communications Society;2024

2. Internet of Things support reliability;Reliability and Resilience in the Internet of Things;2024

3. Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks;ICC 2022 - IEEE International Conference on Communications;2022-05-16

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