Adversarial Score-Based Generative Models for MMSE-Achieving AmBC Channel Estimation-Reference-Cited by-同舟云学术

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Adversarial Score-Based Generative Models for MMSE-Achieving AmBC Channel Estimation

Published:2024-04 Issue:4 Volume:13 Page:1053-1057
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Rezaei Fatemeh¹^ORCID,Marvasti-Zadeh S. Mojtaba²^ORCID,Tellambura Chintha¹^ORCID,Maaref Amine³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

2. Department of Computing Science and Renewable Resources, University of Alberta, Edmonton, Canada

3. Ottawa Wireless Advanced System Competency Centre, Huawei Canada, Ottawa, Canada

Funder

Huawei Technologies Canada Company, Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962382/10496347/10415876.pdf?arnumber=10415876

Reference19 articles.

1. Coding Techniques for Backscatter Communications—A Contemporary Survey

2. Fading Statistics for Multi-Antenna RF Tags

3. Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

4. Deep Learning for Channel Estimation: Interpretation, Performance, and Comparison

5. MIMO Channel Estimation using Score-Based Generative Models

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems;IEEE Transactions on Machine Learning in Communications and Networking;2024

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