{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:54:57Z","timestamp":1762005297844,"version":"build-2065373602"},"reference-count":87,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate\u2014using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards\u2014that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.<\/jats:p>","DOI":"10.3390\/s21072414","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T21:37:03Z","timestamp":1617226623000},"page":"2414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3445-0467","authenticated-orcid":false,"given":"Ebtesam","family":"Almazrouei","sequence":"first","affiliation":[{"name":"Emirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5186-0199","authenticated-orcid":false,"given":"Gabriele","family":"Gianini","sequence":"additional","affiliation":[{"name":"Emirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5091-3030","authenticated-orcid":false,"given":"Nawaf","family":"Almoosa","sequence":"additional","affiliation":[{"name":"Emirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[{"name":"Emirates ICT Innovation Centre, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Dipartimento di Informatica, Universit\u00e0 degli Studi di Milano, 20133 Milano, Italy"},{"name":"Research Centre on Cyber-Physical Systems (C2PS), Khalifa University, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/COMST.2016.2593666","article-title":"Coexistence of LTE-LAA and Wi-Fi on 5 GHz with corresponding deployment scenarios: A survey","volume":"19","author":"Chen","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"89714","DOI":"10.1109\/ACCESS.2019.2926197","article-title":"Evaluating unlicensed LTE technologies: LAA vs LTE-U","volume":"7","author":"Giupponi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"153027","DOI":"10.1109\/ACCESS.2020.3016036","article-title":"Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities & Challenges","volume":"8","author":"Naik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MWC.2016.1500356WC","article-title":"Machine learning paradigms for next-generation wireless networks","volume":"24","author":"Jiang","year":"2017","journal-title":"IEEE Wirel. Commun."},{"key":"ref_5","unstructured":"Hessar, M., Najafi, A., Iyer, V., and Gollakota, S. (2020, January 25\u201327). TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds. Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), Santa Clara, CA, USA."},{"key":"ref_6","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/TCCN.2018.2881442","article-title":"A very brief introduction to machine learning with applications to communication systems","volume":"4","author":"Simeone","year":"2018","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016, January 2\u20135). Convolutional radio modulation recognition networks. Proceedings of the International Conference on Engineering Applications of Neural Networks, Aberdeen, UK.","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jagannath, J., Polosky, N., O\u2019Connor, D., Theagarajan, L.N., Sheaffer, B., Foulke, S., and Varshney, P.K. (2018, January 20\u201324). Artificial neural network based automatic modulation classification over a software defined radio testbed. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422346"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Schmidt, M., Block, D., and Meier, U. (2017, January 24\u201326). Wireless interference identification with convolutional neural networks. Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Emden, Germany.","DOI":"10.1109\/INDIN.2017.8104767"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18484","DOI":"10.1109\/ACCESS.2018.2818794","article-title":"End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications","volume":"6","author":"Kulin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An introduction to deep learning for the physical layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","article-title":"Power of deep learning for channel estimation and signal detection in OFDM systems","volume":"7","author":"Ye","year":"2017","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_15","unstructured":"O\u2019Shea, T.J., Erpek, T., and Clancy, T.C. (2017). Deep learning based MIMO communications. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, X., Long, F., Wang, J., Fu, N., Ou, W., and Liu, B. (2017, January 14\u201319). Signal detection of MIMO-OFDM system based on auto encoder and extreme learning machine. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966042"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ye, H., Li, G.Y., Juang, B.H.F., and Sivanesan, K. (2018, January 9\u201313). Channel agnostic end-to-end learning based communication systems with conditional GAN. Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOMW.2018.8644250"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/JSAC.2019.2904352","article-title":"Spatial deep learning for wireless scheduling","volume":"37","author":"Cui","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep learning in mobile and wireless networking: A survey","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Almazrouei, E., Gianini, G., Almoosa, N., and Damiani, E. (2020, January 16\u201320). What Can Machine Learning Do for Radio Spectrum Management?. Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet\u201920, Alicante, Spain.","DOI":"10.1145\/3416013.3426443"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3039","DOI":"10.1109\/COMST.2019.2926625","article-title":"Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial","volume":"21","author":"Chen","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.1109\/JSAC.2019.2933969","article-title":"Machine Learning in the Air","volume":"37","author":"Sidiropoulos","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5663","DOI":"10.1109\/TSP.2018.2868322","article-title":"Neural Network Detection of Data Sequences in Communication Systems","volume":"66","author":"Farsad","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/MCOM.2017.1700200","article-title":"Electrosense: Open and Big Spectrum Data","volume":"56","author":"Rajendran","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_25","unstructured":"Shi, L., Bahl, P., and Katabi, D. (2015, January 17\u201319). Beyond Sensing: Multi-GHz Realtime Spectrum Analytics. Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15), Boston, MA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Terry, B.C., Orange, A., Patwari, N., Kasera, S., and Van Der Merwe, J. (2020, January 4). Spectrum Monitoring and Source Separation in POWDER. Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, WiNTECH\u201920, New York, NY, USA.","DOI":"10.1145\/3411276.3412192"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Chandrasekaran, V., Banerjee, S., and Giustiniano, D. (2019, January 21\u201325). A Framework for Analyzing Spectrum Characteristics in Large Spatio-Temporal Scales. Proceedings of the 25th Annual International Conference on Mobile Computing and Networking, MobiCom\u201919, Los Cabos, Mexico.","DOI":"10.1145\/3300061.3345450"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1109\/TMC.2017.2716936","article-title":"Enabling a Nationwide Radio Frequency Inventory Using the Spectrum Observatory","volume":"17","author":"Zheleva","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1007\/s11227-017-2017-7","article-title":"Anomaly detection of spectrum in wireless communication via deep auto-encoders","volume":"73","author":"Feng","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_30","unstructured":"O\u2019Shea, T.J., Clancy, T.C., and McGwier, R.W. (2016). Recurrent Neural Radio Anomaly Detection. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Das, R., Gadre, A., Zhang, S., Kumar, S., and Moura, J.M.F. (2018, January 20\u201324). A Deep Learning Approach to IoT Authentication. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422832"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/MCOM.2018.1800153","article-title":"Deep Learning Convolutional Neural Networks for Radio Identification","volume":"56","author":"Riyaz","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Brik, V., Banerjee, S., Gruteser, M., and Oh, S. (2008, January 8\u201312). Wireless device identification with radiometric signatures. Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, San Francisc, CA, USA.","DOI":"10.1145\/1409944.1409959"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., West, N., Vondal, M., and Clancy, T.C. (2017, January 19\u201322). Semi-supervised radio signal identification. Proceedings of the 2017 19th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea.","DOI":"10.23919\/ICACT.2017.7890052"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","article-title":"Over-the-air deep learning based radio signal classification","volume":"12","author":"Roy","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016, January 6\u20138). Unsupervised representation learning of structured radio communication signals. Proceedings of the 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), Aalborg, Denmark.","DOI":"10.1109\/SPLIM.2016.7528397"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNNLS.2018.2850703","article-title":"Modulation Classification Based on Signal Constellation Diagrams and Deep Learning","volume":"30","author":"Peng","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TCCN.2018.2835460","article-title":"Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors","volume":"4","author":"Rajendran","year":"2018","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TVT.2019.2951594","article-title":"Deep Neural Network for Robust Modulation Classification Under Uncertain Noise Conditions","volume":"69","author":"Hu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1109\/TCOMM.2020.2971212","article-title":"Intelligent sharing for LTE and WiFi Systems in Unlicensed Bands: A Deep Reinforcement Learning Approach","volume":"68","author":"Tan","year":"2020","journal-title":"IEEE Trans. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Selim, A., Paisana, F., Arokkiam, J.A., Zhang, Y., Doyle, L., and DaSilva, L.A. (2017, January 4\u20138). Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks. Proceedings of the GLOBECOM 2017\u20142017 IEEE Global Communications Conference, Singapore.","DOI":"10.1109\/GLOCOM.2017.8254105"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nika, A., Zhang, Z., Zhou, X., Zhao, B.Y., and Zheng, H. (2014, January 11). Towards Commoditized Real-Time Spectrum Monitoring. Proceedings of the 1st ACM Workshop on Hot Topics in Wireless, HotWireless\u201914, Maui, HI, USA.","DOI":"10.1145\/2643614.2643615"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rayanchu, S., Patro, A., and Banerjee, S. (2011, January 2\u20134). Airshark: Detecting Non-WiFi RF Devices Using Commodity WiFi Hardware. Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Berlin, Germany.","DOI":"10.1145\/2068816.2068830"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hong, S.S. (2011, January 15\u201319). DOF: A Local Wireless Information Plane. Proceedings of the ACM SIGCOMM 2011 Conference, SIGCOMM\u201911, Toronto, ON, Canada.","DOI":"10.1145\/2018436.2018463"},{"key":"ref_45","unstructured":"Guddeti, Y., Subbaraman, R., Khazraee, M., Schulman, A., and Bharadia, D. (2019, January 16\u201318). Sweepsense: Sensing 5 ghz in 5 milliseconds with low-cost radios. Proceedings of the 16th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 19), Santa Clara, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Roy, T., and Erpek, T. (September, January 28). Spectral detection and localization of radio events with learned convolutional neural features. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081223"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bitar, N., Muhammad, S., and Refai, H.H. (2017, January 8\u201313). Wireless technology identification using deep Convolutional Neural Networks. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292183"},{"key":"ref_48","unstructured":"Abadi, M., and Andersen, D.G. (2016). Learning to protect communications with adversarial neural cryptography. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Grunau, S., Block, D., and Meier, U. (2018, January 18\u201320). Multi-Label Wireless Interference Classification with Convolutional Neural Networks. Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal.","DOI":"10.1109\/INDIN.2018.8471956"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1109\/TCCN.2020.2985375","article-title":"WiST ID -Deep Learning-Based Large Scale Wireless Standard Technology Identification","volume":"6","author":"Behura","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_51","unstructured":"Danev, B., and Capkun, S. (2009, January 13\u201316). Transient-Based Identification of Wireless Sensor Nodes. Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, IPSN\u201909, San Francisco, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MCOM.2017.1600863","article-title":"Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges","volume":"55","author":"Tran","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ferdman, M., and Milder, P. (2017, January 24\u201328). Maximizing CNN accelerator efficiency through resource partitioning. Proceedings of the 2017 ACM\/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), Toronto, ON, Canada.","DOI":"10.1145\/3079856.3080221"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Alizadeh Vahid, K., Prabhu, A., Farhadi, A., and Rastegari, M. (2020, January 14\u201319). Butterfly Transform: An Efficient FFT Based Neural Architecture Design. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01204"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_57","unstructured":"Mertins, A., and Mertins, D.A. (1999). Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications, John Wiley & Sons, Inc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/3417084.3417090","article-title":"Towards Low-Cost, Ubiquitous High-Time Resolution Sensing for Terrestrial Spectrum","volume":"24","author":"Guddeti","year":"2020","journal-title":"Getmobile Mob. Comp. Comm."},{"key":"ref_59","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mio, C., and Gianini, G. (July, January 29). Signal reconstruction by means of Embedding, Clustering and AutoEncoder Ensembles. Proceedings of the 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain.","DOI":"10.1109\/ISCC47284.2019.8969655"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.nima.2017.12.050","article-title":"Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography","volume":"884","author":"Lee","year":"2018","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrom. Detect. Assoc. Equip."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Almazrouei, E., Gianini, G., Almoosa, N., and Damiani, E. (2019, January 15\u201318). A Deep Learning Approach to Radio Signal Denoising. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW), Marrakech, Morocco.","DOI":"10.1109\/WCNCW.2019.8902756"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Almazrouei, E., Gianini, G., Mio, C., Almoosa, N., and Damiani, E. (2019, January 25\u201329). Using AutoEncoders for Radio Signal Denoising. Proceedings of the 15th ACM International Symposium on QoS and Security for Wireless and Mobile Networks, Miami Beach, FL, USA.","DOI":"10.1145\/3345837.3355949"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/26.664294","article-title":"Algorithms for automatic modulation recognition of communication signals","volume":"46","author":"Nandi","year":"1998","journal-title":"IEEE Trans. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/3036699.3036705","article-title":"A survey of LTE Wi-Fi coexistence in unlicensed bands","volume":"20","author":"Wang","year":"2017","journal-title":"Getmob. Mob. Comput. Commun."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MWC.2016.7422404","article-title":"IEEE 802.11 ax: High-efficiency WLANs","volume":"23","author":"Bellalta","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/MCOM.001.1900338","article-title":"IEEE 802.11 be Extremely High Throughput: The Next Generation of Wi-Fi Technology Beyond 802.11 ax","volume":"57","author":"Kasslin","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_71","first-page":"363","article-title":"Core vector machines: Fast SVM training on very large data sets","volume":"6","author":"Tsang","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_75","unstructured":"Lim, H., Park, J., and Han, Y. (2017, January 16\u201317). Rare sound event detection using 1D convolutional recurrent neural networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop, Munich, Germany."},{"key":"ref_76","unstructured":"Powers, D.M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_77","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_79","unstructured":"Erceg, V., Schumacher, L., and Kyritsi, P. (2004). IEEE 802.11 Document 03\/940r4 (TGn Channel Models), IEEE."},{"key":"ref_80","unstructured":"Breit, G., Sampath, H., and Vermani, S. (2009). TGac channel model addendum support material. Mentor IEEE, Doc IEEE 802.11-09\/06\/0569r0, IEEE."},{"key":"ref_81","unstructured":"Liu, J., Porat, R., and Jindal, N. (2014). IEEE 802.11 ax channel model document. Wireless LANs, Rep. IEEE, IEEE."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/35.1000232","article-title":"A comparison of the HIPERLAN\/2 and IEEE 802.11 a wireless LAN standards","volume":"40","author":"Doufexi","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_83","unstructured":"Jianhan Liu, R.P. (2014). TGax Channel Model. IEEE 802.11-14\/0882r4, IEEE."},{"key":"ref_84","unstructured":"Rappaport, T.S. (1996). Wireless Communications: Principles and Practice, Prentice Hall PTR."},{"key":"ref_85","unstructured":"(2011). User Equipment (UE) Conformance Specification Radio, ETSI. Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) Conformance Specification Radio Transmission and Reception; 3rd Generation Partnership Project; Technical Specification Group Radio Access Network."},{"key":"ref_86","unstructured":"Lte, E. (2009). Evolved Universal Terrestrial Radio Access (e-Utra); Base Station (bs) Radio Transmission and Reception (3gpp ts 36.104 Version 8.6. 0 Release 8), July 2009, ETSI."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1109\/TWC.2009.080769","article-title":"Two new sum-of-sinusoids-based methods for the efficient generation of multiple uncorrelated Rayleigh fading waveforms","volume":"8","author":"Patzold","year":"2009","journal-title":"IEEE Trans. Wirel. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:19Z","timestamp":1760362399000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":87,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072414"],"URL":"https:\/\/doi.org\/10.3390\/s21072414","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,4,1]]}}}