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In multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, representing the whole channel only based on partial subchannels will significantly reduce the channel acquisition overhead.
“Deep learning for massive MIMO CSI feedback,”
C.-K. Wen, W.-T. Shih, and S. Jin, · 2018
Earlier work this paper cites.
“Deep learning for TDD and FDD massive MIMO: Mapping channels in space and frequency,”
M. Alrabeiah and A. Alkhateeb, · 2019
Earlier work this paper cites.
“Deep learning-based channel estimation,”
M. Soltani, V. Pourahmadi, A. Mirzaei, et al., · 2019
Earlier work this paper cites.
“DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications,”
A. Alkhateeb, · 2019
Cited alongside, same era.
“Deep residual learning meets OFDM channel estimation,”
L. Li, H. Chen, H.-H. Chang, et al., · 2020
Cited alongside, same era.
“MLP-Mixer: An all-MLP architecture for vision,”
I.O. Tolstikhin, N. Houlsby, A. Kolesnikov, et al., · 2021
Cited alongside, same era.
“Big AI models for 6G wireless networks: Opportunities, challenges, and research directions,”
Z. Chen, Z. Zhang, and Z. Yang, · 2023
Later among the works it cites.
“Viewing channel as sequence rather than image: A 2-D Seq2Seq approach for efficient MIMO-OFDM CSI feedback,”
Z. Chen, Z. Zhang, Z. Xiao, et al., · 2023
Later among the works it cites.
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