In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models-Reference-Cited by-同舟云学术

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In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models

Published:2024-06-09 Issue: Volume: Page:1573-1578
ISSN:
Container-title:2024 IEEE International Conference on Communications Workshops (ICC Workshops)
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Author:

Zecchin Matteo¹,Yu Kai²,Simeone Osvaldo¹

Affiliation:

1. King's College London,King's Communications, Learning & Information Processing (KCLIP) lab within the Centre for Intelligent Information Processing Systems (CIIPS),Department of Engineering,London,U.K.,WC2R 2LS

2. School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10615255/10615259/10615360.pdf?arnumber=10615360

Reference23 articles.

1. Implementing and evaluating security in O- RAN: Interfaces, intelligence, and platforms;Groen;arXiv preprint,2023

2. From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems

3. Learning with Limited Samples: Meta-Learning and Applications to Communication Systems

4. A survey for in-context learning;Dong;arXiv preprint,2022

5. A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems

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