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Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance.
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“Grad-CAM: visual explanations from deep networks via gradient-based localization,”
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, · 2017
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“Proximal Policy Optimization Algorithms,”
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, · 2017
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“End-to-end learning of communications systems without a channel model,”
F. A. Aoudia and J. Hoydis, · 2018
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“Correlation based feature selection algorithm for machine learning,”
N. Gopika and A. M. K. ME, · 2018
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“A correlation-based feature weighting filter for naive bayes,”
L. Jiang, L. Zhang, C. Li, and J. Wu, · 2018
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“Learning semantic concepts and order for image and sentence matching,”
Y. Huang, Q. Wu, C. Song, and L. Wang, · 2018
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“Deep joint source-channel coding for wireless image transmission,”
E. Bourtsoulatze, D. Burth Kurka, and D. Gunduz, · 2019
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“Deep learning in physical layer communications,”
Z. Qin, H. Ye, G. Y. Li, and B. F. Juang, · 2019
Cited alongside, same era.
“Learning task-oriented communication for edge inference: An information bottleneck approach,”
J. Shao, Y. Mao, and J Zhang, · 2021
Cited alongside, same era.
“Deep Learning Enabled Semantic Communication Systems,”
H. Xie, Z. Qin, G. Y. Li, and B. Juang, · 2021
Cited alongside, same era.
“Task-oriented multi-user semantic communications for VQA,”
H. Xie, Z. Qin, and G. Y. Li, · 2021
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“Content driven and reinforcement learning based resource allocation scheme in vehicular network,”
J. Chen, C. Guo, C. Feng, M. Zhu, and Q. Sun, · 2021
Cited alongside, same era.
“Semantic communications with artificial intelligence tasks: Reducing bandwidth requirements and improving artificial intelligence task performance,”
“Wireless semantic communications for video conferencing,”
P. Jiang, C. L. Wen, S. Jin, and G. Y. Li, · 2022
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“Massive MIMO-OFDM systems with low resolution ADCs: Cramér-Rao bound, sparse channel estimation, and soft symbol decoding,”
S. S. Thoota and C. R. Murthy, · 2022
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“Drl-driven dynamic resource allocation for task-oriented semantic communication,”
H. Zhang, H. Wang, Y. Li, K. Long, and A. Nallanathan, · 2023
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“Curriculum learning for goal-oriented semantic communications with a common language,”
M. K. Farshbafan, W. Saad, and M. Debbah, · 2023
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“Robust semantic communications with masked VQ-VAE enabled codebook,”
Q. Hu, G. Zhang, Z. Qin, Y. Cai, G. Yu, and G. Y. Li, · 2023
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“Vector quantized semantic communication system,”
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Y. Yang, C. Guo, F. Liu, C. Liu, L. Sun, Q. Sun, and J. Chen, · 2022
Cited alongside, same era.
“All-in-One: VQ-VAE for end-to-end joint source-channel coding,”
M. Nemati and J. Choi, · 2022
Cited alongside, same era.
“Learning based joint coding-modulation for digital semantic communication systems,”
Y. Bo, Y. Duan, S. Shao, and M. Tao, · 2022
Cited alongside, same era.
“Towards semantic communications: Deep learning-based image semantic coding,”
D. Huang, F. Gao, X. Tao, Q. Du, and J. Lu, · 2022
Cited alongside, same era.
Q. Fu, H. Xie, Z. Qin, G. Slabaugh, and X. Tao, · 2023
Later among the works it cites.
L. Guo, W. Chen, Y. Sun, and B. Ai, · 2023
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“Design and analysis of hardware-limited non-uniform task-based quantizers,”
N. I. Bernardo, J. Zhu, Y. C. Eldar, and J. Evans, · 2023
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“Adaptive information bottleneck guided joint source and channel coding for image transmission,”
L. Sun, Y. Yang, M. Chen, C. Guo, W. Saad, and H. V. Poor, · 2023
Later among the works it cites.