Online Meta-Learning for Hybrid Model-Based Deep Receivers

Author:

Raviv Tomer1ORCID,Park Sangwoo2ORCID,Simeone Osvaldo2ORCID,Eldar Yonina C.3ORCID,Shlezinger Nir1ORCID

Affiliation:

1. School of ECE, Ben-Gurion University of the Negev, Be’er Sheva, Israel

2. Department of Engineering, King’s College London, London, U.K.

3. Faculty of Math and CS, Weizmann Institute of Science, Rehovot, Israel

Funder

Israeli Wireless Intelligent Network (5G-WIN) Consortium

European Union’s Horizon 2020 Research and Innovation Program

Israel Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Cited by 7 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. Latent-KalmanNet: Learned Kalman Filtering for Tracking From High-Dimensional Signals;IEEE Transactions on Signal Processing;2024

2. Learn to Rapidly and Robustly Optimize Hybrid Precoding;IEEE Transactions on Communications;2023-10

3. Unsupervised Deep-Learning for Distributed Clock Synchronization in Wireless Networks;IEEE Transactions on Vehicular Technology;2023-09

4. Learned Kalman Filtering in Latent Space with High-Dimensional Data;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

5. Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers;2023 IEEE International Conference on Communications Workshops (ICC Workshops);2023-05-28

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