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Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution.
“Deep learning for massive MIMO CSI feedback,”
C.-K. Wen, W.-T. Shih, and S. Jin, · 2018
Earlier work this paper cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding,”
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, · 2018
Earlier work this paper cites.
“Fingerprint-based localization for massive MIMO-OFDM system with deep convolutional neural networks,”
X. Sun, C. Wu, X. Gao, and G. Y. Li, · 2019
Earlier work this paper cites.
“Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,”
Y. S. Nasir and D. Guo, · 2019
Earlier work this paper cites.
“DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications,”
A. Alkhateeb, · 2019
Earlier work this paper cites.
“Language models are few-shot learners,”
T. Brown, B. Mann, N. Ryder, et al., · 2020
Cited alongside, same era.
“MIMO-GAN: Generative MIMO channel modeling,”
T. Orekondy, A. Behboodi, and J. B. Soriaga, · 2022
Cited alongside, same era.
“C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction,”
Z. Xiao, Z. Zhang, C. Huang, et al., · 2022
Cited alongside, same era.
“Framework and overall objectives of the future development of IMT for 2030 and beyond,”
ITU Radiocommunication Study Groups, · 2023
Cited alongside, same era.
“Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,”
M. Xu, H. Du, D. Niyato, et al., · 2023
Cited alongside, same era.
“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
Closest in time.
“Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,”
W. Xu, Z. Yang, D. W. K. Ng, et al., · 2023
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“A bargaining game for personalized, energy efficient split learning over wireless networks,”
M. Kim, A. DeRieux, and W. Saad, · 2023
Closest in time.
“Reliability-aware flow distribution algorithm in SDN-enabled fog computing for smart cities,”
M. Ibrar, L. Wang, N. Shah, et al., · 2023
Closest in time.
“Channel mapping based on interleaved learning with complex-domain MLP-Mixer,”
Z. Chen, Z. Zhang, Z. Yang, and L. Liu, · 2024
Closest in time.
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