Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression
Tài liệu tham khảo
Jagannath, 2021, Redefining wireless communication for 6G: Signal processing meets deep learning with deep unfolding, IEEE Trans. Artif. Intell., 10.1109/TAI.2021.3108129
2019
2019
Sohul, 2015, Spectrum access system for the citizen broadband radio service, IEEE Commun. Mag., 53, 18, 10.1109/MCOM.2015.7158261
Zhou, 2020, Deep learning for modulation recognition: A survey with a demonstration, IEEE Access, 8, 67366, 10.1109/ACCESS.2020.2986330
Jagannath, 2020, Neural networks for signal intelligence: Theory and practice,
Li, 2019, A survey on deep learning techniques in wireless signal recognition, Wirel. Comms. Mobile Comput., 2019, 1, 10.1155/2019/2757601
Jagannath, 2022
Peng, 2019, Modulation classification based on signal constellation diagrams and deep learning, IEEE Trans. Neural Netw. Learn. Syst., 30, 718, 10.1109/TNNLS.2018.2850703
J. Jagannath, N. Polosky, D.O. Connor, L. Theagarajan, B. Sheaffer, S. Foulke, P. Varshney, Artificial Neural Network based Automatic Modulation Classifier for Software Defined Radios. in: Proc. of IEEE Intl, Conf. on Communications (ICC), 2018, Kansas City, USA.
O’Shea, 2018, Over-the-air deep learning based radio signal classification, IEEE J. Sel. Top. Sign. Proces., 12, 168, 10.1109/JSTSP.2018.2797022
Hermawan, 2020, CNN-based automatic modulation classification for beyond 5G communications, IEEE Commun. Lett., 24, 1038, 10.1109/LCOMM.2020.2970922
N. Petrov, I. Jordanov, J. Roe, Radar Emitter Signals Recognition and Classification with Feedforward Networks. in: Proc. of the International Conference in Knowledge Based and Intelligent Information and Engineering Systems (KES),Procedia Computer Science, 2013, 22.
A. Jagannath, J. Jagannath, Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification. in: Proc. of IEEE International Conference on Communications (ICC), 2021, Montreal, Canada.
M. Schmidt, D. Block, U. Meier, Wireless interference identification with convolutional neural networks. in: Proc. of the IEEE Intl. Conf. on Industrial Informatics (INDIN), 2017, pp. 180–185.
N. Bitar, S. Muhammad, H.H. Refai, Wireless technology identification using deep Convolutional Neural Networks. in: Proc. of Intl Symp. on Personal, Indoor, and Mobile Radio Comms. (PIMRC), 2017, pp. 1–6.
Jagannath, 2020
Jagannath, 2019, Machine learning for wireless communications in the internet of things: A comprehensive survey, Ad Hoc Netw., 93, 10.1016/j.adhoc.2019.101913
Hazza, 2012, Automatic modulation classification of digital modulations in presence of HF noise, Eurasip J. Adv. Signal Process, 2012, 238, 10.1186/1687-6180-2012-238
Chang, 2015, Cumulants-based modulation classification technique in multipath fading channels, IET Commun., 9, 828, 10.1049/iet-com.2014.0773
Majhi, 2017, Hierarchical hypothesis and feature-based blind modulation classification for linearly modulated signals, IEEE Trans. Veh. Technol., 66, 11057, 10.1109/TVT.2017.2727858
Han, 2016, Low complexity automatic modulation classification based on order-statistics, IEEE Trans. Wireless Commun., PP, 1
Hameed, 2009, On the likelihood-based approach to modulation classification, IEEE Trans. Wireless Commun., 8, 5884, 10.1109/TWC.2009.12.080883
Zheng, 2018, Likelihood-based automatic modulation classification in OFDM with index modulation, IEEE Trans. Veh. Technol., 67, 8192, 10.1109/TVT.2018.2839735
T. Wimalajeewa, J. Jagannath, P.K. Varshney, A. Drozd, W. Su, Distributed asynchronous modulation classification based on hybrid maximum likelihood approach. in: Proc. of IEEE Military Communications Conference (MILCOM), 2015, Tampa, FL.
Y. Zhang, N. Ansari, W. Su, Optimal Decision Fusion Based Automatic Modulation Classification by Using Wireless Sensor Networks in Multipath Fading Channel. in: Proc. of IEEE Global Telecommunications Conference (GLOBECOM), 2011, Houston, TX.
Dulek, 2015, Distributed maximum likelihood classification of linear modulations over nonidentical flat block-fading Gaussian channels, IEEE Trans. Wireless Commun., 14, 724, 10.1109/TWC.2014.2359019
Ozdemir, 2015, Asynchronous linear modulation classification with multiple sensors via generalized EM algorithm, IEEE Trans. Wireless Commun., 14, 6389, 10.1109/TWC.2015.2453269
J. Jagannath, D. O’Connor, N. Polosky, B. Sheaffer, L.N. Theagarajan, S. Foulke, P.K. Varshney, S.P. Reichhart, Design and Evaluation of Hierarchical Hybrid Automatic Modulation Classifier using Software Defined Radios. in: Proc. of IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2017, Las Vegas, NV.
S. Foulke, J. Jagannath, A. Drozd, T. Wimalajeewa, P. Varshney, W. Su, Multisensor Modulation Classification (MMC): Implementation Considerations – USRP Case Study. in: Proc. of IEEE Military Communications Conference (MILCOM), 2014, Baltimore, MD.
J. Jagannath, D. O’Connor, N. Polosky, B. Sheaffer, L.N. Theagarajan, S. Foulke, P.K. Varshney, S.P. Reichhart, Design and Evaluation of Hierarchical Hybrid Automatic Modulation Classifier using Software Defined Radios. in: Proc. of IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2017, Las Vegas, NV, USA.
H.-Y. Liu, J.-C. Sun, A modulation type recognition method using wavelet support vector machines. in: Proc. of IEEE Intl. Congress on Image and Signal Processing (CISP), 2009, Tianjin, China.
Popoola, 2011, A novel modulation-sensing method, IEEE Veh. Technol. Mag., 6, 60, 10.1109/MVT.2011.941893
M.M. Roganovic, A.M. Neskovic, N.J. Neskovic, Application of artificial neural networks in classification of digital modulations for Software Defined Radio. in: Proc. of IEEE EUROCON, 2009, St. Petersburg, Russia.
J.J. Popoola, R. v. Olst, Effect of training algorithms on performance of a developed automatic modulation classification using artificial neural network. in: Proc. of IEEE AFRICON, Pointe-Aux-Piments, 2013, Mauritius.
Li, 2018, Robust automated VHF modulation recognition based on deep convolutional neural networks, IEEE Commun. Lett., 22, 946, 10.1109/LCOMM.2018.2809732
C. Szegedy, . Wei Liu, . Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions. in: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9.
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, in: Proc. of the 25th International Conference on Neural Information Processing Systems - Volume 1, in: NIPS’12, Red Hook, NY, USA, 2012, pp. 1097–1105.
C. Wang, J. Wang, X. Zhang, Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network. in: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2437–2441.
O’Shea, 2016, Radio machine learning dataset generation with GNU radio
Wang, 2019, Data-driven deep learning for automatic modulation recognition in cognitive radios, IEEE Trans. Veh. Technol., 68, 4074, 10.1109/TVT.2019.2900460
Xu, 2018, A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals, 373
O.S. Mossad, M. ElNainay, M. Torki, Deep Convolutional Neural Network with Multi-Task Learning Scheme for Modulations Recognition. in: Proc. of International Wireless Communications Mobile Computing Conference (IWCMC), 2019, pp. 1644–1649.
K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition. in: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
Collobert, 2008, A unified architecture for natural language processing: Deep neural networks with multitask learning, 160
Afouras, 2018, Deep audio-visual speech recognition, IEEE Trans. Pattern Anal. Mach. Intell., 1
Schmidt, 2019
O’Shea, 2016
Reus-Muns, 2020, Trust in 5G open RANs through machine learning: RF fingerprinting on the POWDER PAWR platform
Thrun, 1995, Is learning the N-Th thing any easier than learning the first?, 640
S. Zhao, T. Liu, S. Zhao, F. Wang, A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization. in: Proc. of the AAAI Conference on Artificial Intelligence (AAAI), 2019.
Majumder, 2019, Sentiment and sarcasm classification with multitask learning, IEEE Intell. Syst., 34, 38, 10.1109/MIS.2019.2904691
A. Kendall, Y. Gal, R. Cipolla, Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. in: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018.
Lu, 2015, Semi-supervised multitask learning for scene recognition, IEEE Trans. Cybern., 45, 1967, 10.1109/TCYB.2014.2362959
R. Caruana, Multitask Learning: A Knowledge-Based Source of Inductive Bias. in: Proc. of the Intl. Conf. on Machine Learning, 1993.
Baxter, 1997, A Bayesian/information theoretic model of learning to learn ViaMultiple task sampling, Mach. Learn., 28, 7, 10.1023/A:1007327622663
Standley, 2020, Which tasks should be learned together in multi-task learning?, 9120
Kingma, 2014
Blossom, 2004, GNU radio: Tools for exploring the radio frequency spectrum, Linux J., 2004, 4
Jagannath, 2021, Dataset for modulation classification and signal type classification for multi-task and single task learning, Comput. Netw., 10.1016/j.comnet.2021.108441
Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 448
K. Hazelwood, S. Bird, D. Brooks, S. Chintala, U. Diril, D. Dzhulgakov, M. Fawzy, B. Jia, Y. Jia, A. Kalro, J. Law, K. Lee, J. Lu, P. Noordhuis, M. Smelyanskiy, L. Xiong, X. Wang, Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. in: Proc. of IEEE International Symposium on High Performance Computer Architecture (HPCA), 2018, pp. 620–629.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, D. Kalenichenko, Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. in: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 2704–2713.
2008, Ieee standard for floating-point arithmetic, 1
Lin, 2016, Fixed point quantization of deep convolutional networks, 2849
J. Wu, C. Leng, Y. Wang, Q. Hu, J. Cheng, Quantized Convolutional Neural Networks for Mobile Devices. in: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4820–4828.
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