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Channel modelling is essential to designing modern wireless communication systems.
J. Aráuz and P. Krishnamurthy, “Markov modeling of 802.11 channels,” in 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No. 03CH37484)
2003
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Y. Bengio, E. Laufer, G. Alain, and J. Yosinski, “Deep generative stochastic networks trainable by backprop,” in International Conference on Machine Learning
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J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning
2015
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T. Salimans, D. Kingma, and M. Welling, “Markov chain monte carlo and variational inference: Bridging the gap,” in International conference on machine learning
2015
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A. Smith and J. Downey, “A communication channel density estimating generative adversarial network,” in 2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)
2019
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Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in Neural Information Processing Systems
2019
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T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, “Improved precision and recall metric for assessing generative models,” Advances in Neural Information Processing Systems
2019
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM
2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems
2020
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Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” Advances in neural information processing systems
2020
Cited alongside, same era.
A. Zander, “Applying machine learning for generating radio channel coefficients: Practical insights into the process of selectingand implementing machine learning algorithms for spatial channel modelling,” 2021
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning
2021
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Q. Zhu, C.-X. Wang, B. Hua, K. Mao, S. Jiang, and M. Yao, “3gpp tr 38.901 channel model,” in The Wiley 5G Ref: The Essential 5G Reference Online
2021
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H. Xiao, W. Tian, W. Liu, and J. Shen, “Channelgan: Deep learning-based channel modeling and generating,” IEEE Wireless Communications Letters
2022
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T. Orekondy, A. Behboodi, and J. B. Soriaga, “Mimo-gan: Generative mimo channel modeling,” in ICC 2022-IEEE International Conference on Communications
2022
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2021
Cited alongside, same era.
2022
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