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【2021年更新】面向通信技术的机器学习和深度学习文献汇总_high dimensional channel estimation using deep gen

high dimensional channel estimation using deep generative networks

参考IEEE的Library
附带源码的文献汇总

综述

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信号检测、信号分类和比较

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信道编码和解码

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• H. Lee, T. Q. S. Quek, and S. H. Lee, “A deep learning approach to universal binary visible light communication transceiver,” preprint arXiv:1910.12048, 2019.
• L. Shi, X. Zhang, W. Wang, Y. Zhang, Z. Wang, A. Vladimirescu, Y. Zhang, and J. Wang, “PAPR reduction based on deep autoencoder for VLC DCO-OFDM system,” in Proc. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, 2019.
• A. Sahai, J. Sanz, V. Subramanian, C. Tran and K. Vodrahall, “Learning to communicate with limited co-design,” in Proc. 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019.
• Nuwanthika Rajapaksha, N. Rajatheva and M. Latva-aho, “Low complexity autoencoder based end-to-end learning of coded communications systems,” preprint arXiv:1911.08009, 2019.
• D. B. Kurka, and D. Gündüz, “DeepJSCC-f: Deep joint-source channel coding of images with feedback,” preprint arXiv:1911.11174, 2019.
• S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. T. Brink, “Trainable Communication Systems: Concepts and Prototype,” in IEEE Transactions on Communications, 2020.
• A. Tato and C. Mosquera, “Spatial modulation for beyond 5G communications: Capacity calculation and link adaptation,” Proceedings, 2019.
• R. Daniels and R. W. Heath, Jr., “An online learning framework for link adaptation in wireless networks,” in Proc. Information Theory and Applications Workshop, February 2009.
• M. P. Mota, D. C. Araujo, F. H. C. Neto, A. L. F. de Almeida, and F. R. P. Cavalcanti, “Adaptive modulation and coding based on reinforcement learning for 5G networks,” preprint arXiv:1912.04030, 2019.
• H. Zhang, L. Zhang and Y. Jiang, “Overfitting and underfitting analysis for deep learning based end-to-end communication systems,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.
• L. Li, C. Tellambura and X. Tang, “Improved tone reservation method based on deep learning for PAPR reduction in OFDM system,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.
• M. Zhang, M. Liu and Z. Zhong, “Neural network assisted active constellation extension for PAPR reduction of OFDM system,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.
• Y. Song, M. Xu, L. Yu, H. Zhou, S. Shao, and Y. Yu, “Infomax neural joint source-channel coding via adversarial bit flip,” in Proc. 34th AAAI Conference on Artificial Intelligence (AAAI), 2019.
• J. Xu, W. Chen, B. Ai, R. He, Y. Li, J. Wang, T. Juhana, and A. Kurniawan, “Performance evaluation of autoencoder for coding and modulation in wireless communications,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019.
• K. Gümüs, A. Alvarado, B. Chen, C. Häger, and E. Agrell, “End-to-end learning of geometrical shaping maximizing generalized mutual information,” preprint arXiv:1912.05638, 2019.
• B. Karanov, M. Chagnon, V. Aref, D. Lavery, P. Bayvel, and L. Schmalen, “Concept and experimental demonstration of optical IM/DD end-to-end system optimization using a generative model,” preprint arXiv:1912.05146, 2019.
• E. Sillekens, W. Yi, D. Semrau, A. Ottino, B. Karanov, S. Zhou, K. Law, J. Chen, D. Lavery, L. Galdino, P. Bayvel, and R. I. Killey, “Experimental demonstration of learned time-domain digital back-propagation,” preprint arXiv:1912.12197, 2019.
• S. Park, O. Simeone, and J. Kang, “End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning,” preprint arXiv:2003.01479, 2020.
• S. Dörner, M. Henninger, S. Cammerer, and S. ten Brink, “WGAN-based Autoencoder Training Over-the-air,” preprint arXiv:2003.02744, 2020.
• J. Guo, X. Yang, C.-K. Wen, S. Jin, and G. Ye Li, “DL-based CSI feedback and cooperative recovery in massive MIMO,” preprint arXiv:2003.03303, 2020.
• K. Ullrich, F. Viola, and D. J. Rezende, “Neural Communication Systems with Bandwidth-limited Channel,” preprint arXiv:2003.13367, 2020.
• F. Ait Aoudia and J. Hoydis, “Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems,” preprint arXiv:2004.05062, 2020.
• S. Xue, Y. Ma, N. Yi, and R. Tafazolli, “On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems,” preprint arXiv:2004.06599, 2020.
• M. B. Mashhadi, and D. Gunduz, “Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems,” preprint arXiv:2006.11796, 2020.
• T. Van Luong, Y. Ko, N. A. Vien, M. Matthaiou and H. Q. Ngo, “Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems,” in IEEE Transactions on Wireless Communications, vol. 19, no. 6, pp. 3952-3962, June 2020.
• A. Sahin, D. W. Matolak, “Golay Layer: Limiting Peak-to-Average Power Ratio for OFDM-based Autoencoders,” preprint arXiv:2002.07701, 2020.
• K. Vedula, R. Paffenroth and D. R. Brown, “Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020.
• T. Fujihashi, T. Koike-Akino, S. Chen, and T. Watanabe, “Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks,” preprint arXiv:2006.09835, 2020.
• Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh and P. Viswanath, “Joint Channel Coding and Modulation via Deep Learning,” IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020.
• N. Skatchkovsky, H. Jang, and O. Simeone, “End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence,” preprint arXiv:2009.01527, 2020.
• F. Ait Aoudia and J. Hoydis, “End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication,” preprint arXiv:2009.05261, 2020.
• N. A. Letizia, and A. M. Tonello, “Capacity-Approaching Autoencoders for Communications,” preprint arXiv:2009.05273, 2020.
• D. Burth K., and D. Gündüz, “Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding,” preprint arXiv:2009.12480, 2020.
• J. Xu, B. Ai, W. Chen, A. Yang, and P. Sun, “Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules,” preprint arXiv:2012.00533, 2020.

定位、传感和本地化

• X. Wang, L. Gao, S. Mao and S. Pandey, “CSI-based fingerprinting for indoor localization: a deep learning approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 763-776, January 2017.
• C. Studer, S. Medjkouh, E. Gönültas, T. Goldstein and O. Tirkkonen, “Channel charting: locating users within the radio environment using channel state information,” IEEE Access, vol. 6. pp. 47682-47698, August 2018.
• J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive MIMO fingerprint-based positioning,” in Proc. IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), October 2017.
• K. Davaslioglu and Y. E. Sagduyu, “Generative adversarial learning for spectrum sensing,” 2018 IEEE International Conference on Communications (ICC), May 2018.
• M. Sadegh Safari and V. Pourahmadi, “Deep UL2DL: Channel knowledge transfer from uplink to downlink,” preprint arXiv:1812.07518, 2018.
• M. Arnold, S. Dörner, S. Cammerer, S. Yan, J. Hoydis and S. ten Brink, “Enabling FDD massive MIMO through deep learning-based channel prediction,” preprint arXiv:1901.03664, 2019.
• M. Mehrabi, M. Mohammadkarimi, M. Ardakani and Y. Jing, “Decision directed channel estimation based on deep neural network k-step predictor for MIMO communications in 5G,” preprint arXiv:1901.03435, 2019.
• K. Bregar and M. Mohorčič, “Improving indoor localization using convolutional neural networks on computationally restricted devices,” in IEEE Access, vol. 6, pp. 17429-17441, 2018.
• C. Huang, G. C. Alexandropoulos, A. Zappone, C. Yuen and M. Debbah, “Deep learning for UL/DL channel calibration in generic massive MIMO systems,” in Proc. IEEE International Conference on Communications (ICC), May 2019.
• M. Soltani, V. Pourahmadi, A. Mirzaei and H. Sheikhzadeh, “Deep learning-based channel estimation,” preprint arXiv:1810.05893, 2018. [Simulation code]
• M. Arnold, J. Hoydis and S. ten Brink, “Novel massive MIMO channel sounding data applied to deep learning-based indoor positioning,” preprint arXiv:1810.04126, 2018.
• A. Y. Abyaneh, A. H. G. Foumani and V. Pourahmadi, “Deep neural networks meet CSI-based authentication,” preprint arXiv:1812.04715, 2018.
• P. Yazdanian and V. Pourahmadi, “DeepPos: Deep supervised autoencoder network for CSI based indoor localization,” preprint arXiv:1811.12182, 2018.
• A. Decurninge, L. G. Ordóñez, P. Ferrand, H. Gaoning, L. Bojie, Z. Wei and M. Guillaud, “CSI-based outdoor localization for massive MIMO: experiments with a learning approach,” in Proc. 15th International Symposium on Wireless Communication Systems (ISWCS), August 2018.
• S.-J. Liu, R. Y. Chang and F.-T.Chien, “Analysis and visualization of deep neural networks in device-free Wi-Fi indoor localization,” preprint arXiv:1904.10154, 2018.
• J. Chan, A. Wang, A. Krishnamurthy and S. Gollakota, “DeepSense: Enabling carrier sense in low-power wide area networks using deep learning,” preprint arXiv:1904.10607, 2019.
• J. Xie, C. Liu, Y. Liang and J. Fang, “Activity pattern aware spectrum sensing: A CNN-based deep learning approach,” in IEEE Communications Letters, 2019.
• S. Abeywickrama, L. Jayasinghe, H. Fu, S. Nissanka, and C. Yuen, “RF-based Direction Finding of UAVs Using DNN,” in Proc. IEEE International Conference on Communication Systems (ICCS), 2018. [Simulation code]
• Y. Xu, P. Cheng, Z. Chen, Y. Li and B. Vucetic, “Mobile collaborative spectrum sensing for heterogeneous networks: A bayesian machine learning approach,” in IEEE Transactions on Signal Processing, 2018.
• S. Chaudhari and D. Cabric, “Unsupervised frequency clustering algorithm for null space estimation in wideband spectrum sharing networks,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017.
• Siddhartha, Y. H. Lee, D. J.M. Moss, J. Faraone, P. Blackmore, D. Salmond, D. Boland and P. H.W. Leong, “Long short-term memory for radio frequency spectral prediction and its real-time FPGA implementation,” in Proc. IEEE Military Communications Conference (MILCOM), 2018.
• Z. Ye, A. Gilman, Q. Peng, K. Levick, P. Cosman and L. Milstein, “Comparison of neural network architectures for spectrum sensing,” preprint arXiv:1907.07321, 2019.
• Z. Ye, Q. Peng, K. Levick, H. Rong, A. Gilman, P. Cosman and L. Milstein, “A neural network detector for spectrum sensing under uncertainties,” preprint arXiv:1907.07326, 2019.
• N. Nayak, V. Raj and S. Kalyani, “Leveraging online learning for CSS in frugal IoT network,” preprint arXiv:1907.07201, 2019.
• J. Choi, Y.-S. Choi and S. Talwar, “Unsupervised learning technique to obtain the coordinates of Wi-Fi access points,” preprint arXiv:1907.09514, 2019.
• Y. Xu, P. Cheng, Z. Chen, Y. Hu, Y. Li and B. Vucetic, “Mobile bayesian spectrum learning for heterogeneous networks,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
• H. Sallouha, A. Chiumento, S. Rajendran and S. Pollin, “Localization in ultra narrow band IoT networks: Design guidelines and trade-offs,” preprint arXiv:1907.11205, 2019.
• Q. Peng, A. Gilman, N. Vasconcelos, P. C. Cosman and L. B. Milstein, “Robust deep sensing through transfer learning in cognitive radio,” preprint arXiv:1908.00658, 2019.
• J. Wang, Y. Ding, S. Bian, Y. Peng, M. Liu and G. Gui, “UL-CSI data driven deep learning for predicting DL-CSI in cellular FDD systems,” in IEEE Access, 2019.
• P. Huang, O. Castañeda, E. Gönültaş, S. Medjkouh, O. Tirkkonen, T. Goldstein and C. Studer, “Improving channel charting with representation-constrained autoencoders,” preprint arXiv:1908.02878, 2019.
• C. Liu, J. Wang, X. Liua and Y. Liang, “Deep CM-CNN for spectrum sensing in cognitive radio,” in IEEE Journal on Selected Areas in Communications., 2019.
• J. L. C. V, Z. Zhao, T. Braun and Z. Li, “A particle filter-based reinforcement learning approach for reliable wireless indoor positioning,” in IEEE Journal on Selected Areas in Communications., 2019.
• Y. Yang, F. Gao, G. Y. Li and M. Jian, “Deep learning based downlink channel prediction for FDD massive MIMO system,” preprint arXiv:1908.03360, 2019.
• T. Zhang, S. Liu, W. Xiang; L. Xu, K. Qin and X. Yan, “A real-time channel prediction model based on neural networks for dedicated short-range communications,” Sensors, 2019.
• T. F. Sanam and H. Godrich, “A multi-view discriminant learning approach for indoor localization using bimodal features of CSI,” preprint arXiv:1908.07370, 2019.
• Y. Zhu, X. Dong and T. Lu, “An adaptive and parameter-free recurrent neural structure for wireless channel prediction,” in IEEE Transactions on Communications., 2019.
• J. Gao, X. Yi, C. Zhong, X. Chen and Z. Zhang, “Deep learning for spectrum sensing,” preprint arXiv:1909.02730, 2019.
• E. Lei, O. Castañeda, O. Tirkkonen, T. Goldstein and C. Studer, “Siamese neural networks for wireless positioning and channel charting,” preprint arXiv:1909.13355, 2019.
• Z. Gao, Y. Gao, S. Wang, D. Li, Y. Xu, and H. Jiang, “CRISLoc: Reconstructable CSI fingerprintingfor indoor smartphone localization,” preprint arXiv:1910.06895, 2019.
• M. Najla, Z. Becvar, P. Mach and D. Gesbert, “Predicting device-to-device channels from cellular channel measurements: A learning approach,” preprint arXiv:1911.07191, 2019.
• N. Turan and W. Utschick, “Learning the MMSE channel predictor,” preprint arXiv:1911.07256, 2019.
• M. M. Butt, A. Rao, and D. Yoon, “RF fingerprinting and deep learning assisted UE positioning in 5G,” preprint arXiv:2001.00977, 2020.
• P. Ferrand, A. Decurninge, and M. Guillaud, “DNN-based Localization from Channel Estimates: Feature Design and Experimental Results,” preprint arXiv:2004.00363, 2020.
• S. Fan, Y. Wu, C. Han and X. Wang, “Structured Bidirectional LSTM Deep Learning Method For 3D Terahertz Indoor Localization,” in Proc. IEEE Conference on Computer Communications (INFOCOM), 2020.
• T. Gale, T. Šolc, R. Moşoi, M. Mohorčič and C. Fortuna, “Automatic Detection of Wireless Transmissions,” in IEEE Access, vol. 8, pp. 24370-24384, 2020.
• T. Koike-Akino, P. Wang, M. Pajovic, H. Sun and P. V. Orlik, “Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach,” in IEEE Access, vol. 8, 2020.
• K. M. Attiah, F. Sohrabi, and W. Yu, “Deep Learning Approach to Channel Sensing and Hybrid Precoding for TDD Massive MIMO Systems,” preprint arXiv:2011.10709, 2020.
• L. Antsfeld, B. Chidlovskii, and E. Sansano-Sansano, “Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking,” preprint arXiv:2011.10799, 2020.

安全性和鲁棒性

• M. Sadeghi and E. G. Larsson , “Adversarial attacks on deep-learning based radio signal classification,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 213–216, Feb. 2019. [Simulation code]
• Y. Shi, Y. E. Sagduyu, T. Erpek, K. Davaslioglu, Z. Lu and J. H. Li, “Adversarial deep learning for cognitive radio security: Jamming attack and defense strategies,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2018.
• T. Erpek, Y. E. Sagduyu and Y. Shi, “Deep learning for launching and mitigating wireless jamming attacks,” in IEEE Transactions on Cognitive Communications and Networking., 2018.
• K. K. Nguyen, D. T. Hoang, D. Niyato, P. Wang, D. Nguyen and E. Dutkiewicz, “Cyberattack detection in mobile cloud computing: A deep learning approach,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), 2018.
• A. Diro and N. Chilamkurti, “Leveraging LSTM networks for attack detection in fog-to-things communications,” in IEEE Communications Magazine, vol. 56, no. 9, pp. 124-130, Sept. 2018.
• I. Shakeel, “Machine learning based featureless signalling,” in Proc. IEEE Military Communications Conference (MILCOM), October 2018.
• F. B. Mismar and B. L. Evans, “Deep Q-Learning for self-organizing networks fault management and radio performance improvement,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2018. [Simulation code]
• Y. Shi, T. Erpek, Y. E. Sagduyu and J. H. Li, “Spectrum data poisoning with adversarial deep learning,” in Proc. IEEE Military Communications Conference (MILCOM), 2018.
• M. Bensalem, S. Kumar Singh and A. Jukan, “Machine learning techniques to detecting and preventing jamming attacks in optical networks,” preprint arXiv:1902.07537, 2019.
• M. Sadeghi and E. G. Larsson, “Physical adversarial attacks against end-to-end autoencoder communication systems,” IEEE Communications Letters, 2019. [Simulation code]
• R. Fritschek, R. F. Schaefer and G. Wunder, “Deep learning for the Gaussian wiretap channel,” preprint arXiv:1810.12655, 2018.
• M. Pajovic, T. Koike-Akino and P. V. Orlik, “Model-driven deep learning method for jammer suppression in massive connectivity systems,” preprint arXiv:1903.06266, 2019.
• N. V. Huynh, D. N. Nguyen, D. T. Hoang and E. Dutkiewicz, “Jam me if you can”: Defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications,” in IEEE Journal on Selected Areas in Communications., 2019.
• K. Besser, C. R. Janda, P. Lin and E. A. Jorswieck, “Flexible design of finite blocklength wiretap codes by autoencoders,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.
• Z. Luo, S. Zhao, Z. Lu, J. Xu and Y. E. Sagduyu, “When attackers meet AI: Learning-empowered attacks in cooperative spectrum sensing,” preprint arXiv:1905.01430, 2019.
• Y. Shi, K. Davaslioglu and Y. E. Sagduyu”Generative adversarial network for wireless signal spoofing“, preprint arXiv:1905.01008, 2019.
• S. Rajendran, W. Meert, V. Lenders and S. Pollin, “SAIFE: Unsupervised wireless spectrum anomaly detection with interpretable features,” in Proc. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2018.
• S. Rajendran, V. Lenders, W. Meert and S. Pollin, “Crowdsourced wireless spectrum anomaly detection,” preprint arXiv:1903.05408, 2019.
• S. Rajendran, W. Meert, V. Lenders and S. Pollin, “Unsupervised wireless spectrum anomaly detection with interpretable features,” in IEEE Transactions on Cognitive Communications and Networking., 2019.
• D. Roy, T. Mukherjee, M. Chatterjee and E. Pasiliao, “Detection of rogue RF transmitters using generative adversarial nets,” in proc. IEEE WCNC, 2019.
• Y. E. Sagduyu, Y. Shi and T. Erpek, “IoT network security from the perspective of adversarial deep learning,” preprint arXiv:1906.00076, 2019.
• M. Bensalem, S. K. Singh and A. Jukan, “On detecting and preventing jamming attacks with machine learning in optical networks,” preprint arXiv:1902.07537, 2019.
• D. J. M. Moss, D. Boland, P. Pourbeik and P. H. W. Leong, “Real-time FPGA-based anomaly detection for radio frequency signals,” IEEE International Symposium on Circuits and Systems (ISCAS), 2018.
• F. Shu, L. Liu, Y. Zhang, G. Xia, X. Liu, J. Li, S. Jin and J. Wang, “A deep-learning-based joint inference for secure spatial modulation receiver,” preprint arXiv:1907.02215, 2019.
• F. Jameel, W. U. Khan, Z. Chang, T. Ristaniemi and J. Liu, “Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems,” preprint arXiv:1907.05753, 2019.
• J. Yu, A. Hu, F. Zhou, Y. Xing, Y. Yu, G. Li and L. Peng, “Radio frequency fingerprint identification based on denoising autoencoders,” preprint arXiv:1907.08809, 2019.
• B. Liu, Z. Wei, J. Yuan and M. Pajovic, “Deep learning assisted user identification in massive machine-type communications,” preprint arXiv:1907.09735, 2019.
• M. Usama, J. Qadir and A. Al-Fuqaha, “Black-box adversarial ML attack on modulation classification,” preprint arXiv:1908.00635, 2019.
• A. Anderson, S. R. Young, F. K. Reed and J. M. Vann, “Deep modulation (Deepmod): A self-taught PHY layer for resilient digital communications,” preprint arXiv:1908.11218, 2019.
• R. Yao, Y. Zhang, S. Wang, N. Qi, N. I. Miridakis and T. A. Tsiftsis, “Deep neural network assisted approach for antenna selection in untrusted relay networks,” in IEEE Wireless Communications Letters., 2019.
• U. Masood, A. Asghar, A. Imran and A. N. Mian, “Deep learning based detection of sleeping cells in next generation cellular networks,” in Proc. IEEE Global Communications Conference (GLOBECOM), 2018.
• X. Zhang and M. Vaezi, “Deep learning based precoding for the MIMO Gaussian wiretap channel,” preprint arXiv:1909.07963, 2019.
• M. Usama, M. Asim, J. Qadir, A. Al-Fuqaha and M. Ali Imran, “Adversarial machine learning attack on modulation classification,” preprint arXiv:1909.12167, 2019.
• K. Davaslioglu and Y. E. Sagduyu, “Trojan attacks on wireless signal classification with adversarial machine learning,” preprint arXiv:1910.10766, 2019.
• Y. E. Sagduyu, Y. Shi, and T. Erpek, “Adversarial deep learning for over-the-air spectrum poisoning attacks,” preprint arXiv:1911.00500, 2019.
• D. T. Hoang, D. N. Nguyen, M. A. Alsheikh, S. Gong, E. Dutkiewicz, D. Niyato, and Z. Han, “Borrowing arrows with thatched boats”: The art of defeating reactive jammers in IoT networks,” preprint arXiv:1912.11170, 2019.
• L. Senigagliesi, M. Baldi and E. Gambi, “Performance of statistical and machine learning techniques for physical layer authentication,” preprint arXiv:2001.06238 2020.
• B. Kim, Y. E. Sagduyu, K. Davaslioglu, T. Erpek, and S. Ulukus, “Over-the-air adversarial attacks on deep learning based modulation classifier over wireless channels,” preprint arXiv:2002.02400, 2020.
• Q. Liu, J. Guo, C.-K. Wen, and S. Jin, “Adversarial attack on DL-based massive MIMO CSI feedback,” preprint arXiv:2002.09896, 2020.
• Y. Arjoune, F. Salahdine, M. S. Islam, E. Ghribi, and N. Kaabouch, “A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication,” preprint arXiv:2003.07308 2020.
• N. Abuzainab et al., “QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning,” in Proc. IEEE Military Communications Conference (MILCOM), Norfolk, VA, USA, 2019.
• M. Z. Hameed, A. Gyorgy, and D. Gunduz, “The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection,” preprint arXiv:1902.10674, 2019.
• Z. Utkovski, P. Agostini, M. Frey, I. Bjelakovic and S. Stanczak, “Learning Radio Maps for Physical-Layer Security in the Radio Access,” IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019.
• B. Kim, Y. E. Sagduyu, T. Erpek, K. Davaslioglu, and S. Ulukus, “Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers,” preprint arXiv:2007.16204, 2020.
• J. Stankowicz, J. Robinson, J. M. Carmack and S. Kuzdeba, “Complex Neural Networks for Radio Frequency Fingerprinting,” IEEE Western New York Image and Signal Processing Workshop (WNYISPW), 2019.
• Q. Zhu and L. Sun, “Big Data Driven Anomaly Detection for Cellular Networks,” in IEEE Access, vol. 8, pp. 31398-31408, 2020.
• M. Liu, and R. Wang, “Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel,” preprint arXiv:2011.03750, 2020.
• L. Senigagliesi, M. Baldi, and E. Gambi, “Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication,” preprint arXiv:2001.06238, 2020.
• R. Kolcun, D. A. Popescu, V. Safronov, P. Yadav, A. M. Mandalari, Y. Xie, R. Mortier., and H. Haddadi, “The Case for Retraining of ML Models for IoT Device Identification at the Edge,” preprint arXiv:2011.08605, 2020.
• G. Cerar, H. Yetgin, B. Bertalanič, and C. Fortuna, “Learning to Detect Anomalous Wireless Links in IoT Networks,” preprint arXiv:2008.05232, 2020.

毫米波通信

• X. Li, A. Alkhateeb and C. Tepedelenlioğlu, “Generative adversarial estimation of channel covariance in vehicular millimeter wave systems,” in Proc.Asilomar Conference on Signals, Systems, and Computers, 2018.
• A. Alkhateeb and I.Beltagy, “Machine learning for reliable mmWave systems: Blockage prediction and proactive handoff,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.
• A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu and D. Tujkovic, “Deep learning coordinated beamforming for highly-mobile millimeter wave systems,” in IEEE Access, vol. 6, pp. 37328-37348, 2018. [Simulation code]
• F. B. Mismar and B. L. Evans, “Partially blind handovers for mmWave new radio aided by sub-6 GHz LTE signaling,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), May 2018.
• H. He, C.-K. Wen, S. Jin and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852-855, October 2018. [Simulation code]
• Y. Wang, M. Narasimha and R. W. Heath, Jr., “mmWave beam prediction with situational awareness: a machine learning approach,” in Proc. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), June 2018.
• V. Va, J. Choi, T. Shimizu, G. Bansal and R. W. Heath, “Inverse multipath fingerprinting for millimeter wave V2I beam alignment,” IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4042-4058, May 2018.
• C. Antón-Haro and X. Mestre, “Learning and data-driven beam selection for mmWave communications: An angle of arrival-based approach,” in IEEE Access, vol. 7, pp. 20404-20415, 2019.
• J. Yang, K. Chen, X. Ge, Y. Li and L. Tian, “Neural networks in hybrid precoding for millimeter wave massive MIMO systems,” preprint arXiv:1903.08849, 2019.
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