Odena A., Olah C., Shlens J., Conditional image synthesis with auxiliary classifier GANs, in: Proceedings of the 34th International Conference on Machine Learning, Aug. Ramsundar B., Kearnes S., Riley P., et al., Massively multitask networks for drug discovery, Comput. Szegedy C., Liu W., Jia Y., et al., Going deeper with convolutions, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. Zhu C., Xu L., Liu X.-Y., Qian F., Tensor-generative adversarial network with two-dimensional sparse coding: application to real-time indoor localization, in: IEEE International Conference on Communications (ICC), May 2018. 
Roy D., Mukherjee T., Chatterjee M., Pasiliao E., Detection of rogue RF transmitters using generative adversarial nets, in: IEEE Wireless Communications and Networking Conference (WCNC), Apr. Oshea T.J., Roy T., West N., Hilburn B.C., Physical layer communications system design Over-the-Air using adversarial networks, in: 26th European Signal Processing Conference (EUSIPCO), Sep. Arjovsky M., Chintala S., Bottou L., Wasserstein generative adversarial networks, in: Proceedings of the 34th International Conference on Machine Learning, Aug. Mirza M., Osindero S., Conditional generative adversarial nets, Comput. Radford A., Metz L., Chintala S., Unsupervised representation learning with deep convolutional generative adversarial networks, Comput. Pan Z., Yu W., Yi X., Khan A., Yuan F., Zheng Y., Recent progress on generative adversarial networks (GAN): a survey, IEEE Access 7 ( 2019) 36322– 36333. Goodfellow I., et al., Generative adversarial nets, in: Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), Dec. Schlegl T., Seeböck P., Waldstein S.M., Schmidt-Erfurth U., Langs G., Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, Inf. Yu L., Zhang W., Wang J., Yu Y., SeqGAN: sequence generative adversarial nets with policy gradient, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Feb. Ledig C., Theis L., Huszar F., Caballero J., et al., Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. Gong J., Xu X., Qin Y., Dong W., A generative adversarial network based framework for specific emitter characterization and identification, in: 11th International Conference on Wireless Communications and Signal Processing (WCSP), Oct. Gan Z., Chen L., Wang W., Triangle generative adversarial networks, in: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS), Dec. Spooner C.M., Mody A.N., Chuang J., Petersen J., Modulation recognition using second- and higher-order cyclostationarity, in: 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Mar. Kim K., Akbar I.A., Bae K.K., Um J.-S., Spooner C.M., Reed J.H., Cyclostationary approaches to signal detection and classification in cognitive radio, 2nd IEEE International Symposium on, IEEE, Apr. Li X., Dong F., Zhang S., Guo W., A survey on deep learning techniques in wireless signal recognition, Wirel. 
Kulin M., Kazaz T., Moerman I., De Poorter E., End-to-end learning from spectrum data: a deep learning approach for wireless signal identification in spectrum monitoring applications, IEEE Access 6 ( 2018) 18484– 18501.Du J., Jiang C., Wang J., Ren Y., Debbah Mérouane, Machine learning for 6G wireless networks: carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service, IEEE Veh.
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