{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T19:00:23Z","timestamp":1782241223189,"version":"3.54.5"},"reference-count":81,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3904603"],"award-info":[{"award-number":["2022YFB3904603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.<\/jats:p>","DOI":"10.3390\/s24196494","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:22:48Z","timestamp":1728476568000},"page":"6494","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Non-Line-of-Sight Identification in Cellular Positioning Systems Using a Deep Autoencoding and Generative Adversarial Network Model"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9090-3961","authenticated-orcid":false,"given":"Yanbiao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongliang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/COMST.2019.2951036","article-title":"A Survey on Fusion-Based Indoor Positioning","volume":"22","author":"Guo","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10291-002-0031-5","article-title":"Location-Based Services: Technical and Business Issues","volume":"6","author":"Dao","year":"2002","journal-title":"GPS Solut."},{"key":"ref_3","first-page":"1591","article-title":"Application Status, Development and Future Trend of High-Precision Indoor Navigation and Tracking","volume":"48","author":"Chen","year":"2023","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1109\/TNNLS.2020.3015992","article-title":"Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review","volume":"32","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCOM.001.1900737","article-title":"A Hybrid Positioning System for Location-Based Services: Design and Implementation","volume":"58","author":"Guo","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.2016.1600492CM","article-title":"EdgeIoT: Mobile Edge Computing for the Internet of Things","volume":"54","author":"Sun","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jin, S., Wang, Q., and Dardanelli, G. (2022). A Review on Multi-GNSS for Earth Observation and Emerging Applications. Remote Sens., 14.","DOI":"10.3390\/rs14163930"},{"key":"ref_8","first-page":"256","article-title":"Surveying at the Limits of Local RTK Networks: Test Results from the Perspective of High Accuracy Users","volume":"13","author":"Garrido","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/TITS.2019.2961128","article-title":"GNSS NLOS Exclusion Based on Dynamic Object Detection Using LiDAR Point Cloud","volume":"22","author":"Wen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","first-page":"103783","article-title":"Indoor Localization and Trajectory Correction with Point Cloud-Derived Backbone Map","volume":"129","author":"Zheng","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1109\/TITS.2023.3314836","article-title":"Tightly Coupled Integration of GNSS\/UWB\/VIO for Reliable and Seamless Positioning","volume":"25","author":"Liu","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","unstructured":"Wang, Y., Yang, X., Zhao, Y., Liu, Y., and Cuthbert, L. (2013, January 11\u201314). Bluetooth Positioning Using RSSI and Triangulation Methods. Proceedings of the 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Alarifi, A., Al-Salman, A., Alsaleh, M., Alnafessah, A., Al-Hadhrami, S., Al-Ammar, M.A., and Al-Khalifa, H.S. (2016). Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors, 16.","DOI":"10.3390\/s16050707"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/COMST.2015.2464084","article-title":"Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons","volume":"18","author":"He","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_15","first-page":"103507","article-title":"Multi-Sensor Fusion for Robust Localization with Moving Object Segmentation in Complex Dynamic 3D Scenes","volume":"124","author":"Li","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MCOM.2019.1800543","article-title":"Coverage Enhancement and Fundamental Performance of 5G: Analysis and Field Trial","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Keating, R., S\u00e4ily, M., Hulkkonen, J., and Karjalainen, J. (2019, January 27\u201330). Overview of Positioning in 5G New Radio. Proceedings of the 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland.","DOI":"10.1109\/ISWCS.2019.8877160"},{"key":"ref_18","unstructured":"(2023, August 23). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on NR Positioning Support. Available online: https:\/\/portal.3gpp.org\/desktopmodules\/Specifications\/SpecificationDetails.aspx?specificationId=3501."},{"key":"ref_19","unstructured":"(2023, August 26). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on Channel Model for Frequencies from 0.5 to 100 GHz. Available online: https:\/\/portal.3gpp.org\/desktopmodules\/Specifications\/SpecificationDetails.aspx?specificationId=3173."},{"key":"ref_20","unstructured":"(2023, August 23). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NG Radio Access Network (NG-RAN); Stage 2 Functional Specification of User Equipment (UE) Positioning in NG-RAN. Available online: https:\/\/portal.3gpp.org\/desktopmodules\/Specifications\/SpecificationDetails.aspx?specificationId=3310."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/JSYST.2021.3083103","article-title":"Improving the Positioning Accuracy of UWB System for Complicated Underground NLOS Environments","volume":"16","author":"Cao","year":"2022","journal-title":"IEEE Syst. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11414","DOI":"10.1109\/JIOT.2023.3245144","article-title":"An Adaptive IMU\/UWB Fusion Method for NLOS Indoor Positioning and Navigation","volume":"10","author":"Feng","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8529","DOI":"10.1109\/JIOT.2019.2920081","article-title":"Robust TDOA-Based Localization for IoT via Joint Source Position and NLOS Error Estimation","volume":"6","author":"Wang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_24","unstructured":"(2023, August 23). 3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; 5G System; Location Management Services; Stage 3. Available online: https:\/\/portal.3gpp.org\/desktopmodules\/Specifications\/SpecificationDetails.aspx?specificationId=3407."},{"key":"ref_25","unstructured":"Chen, P.-C. (1999, January 21\u201324). A Non-Line-of-Sight Error Mitigation Algorithm in Location Estimation. Proceedings of the 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), New Orleans, LA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aghaie, N., and Tinati, M.A. (2016, January 10\u201312). Localization of WSN Nodes Based on NLOS Identification Using AOAs Statistical Information. Proceedings of the 2016 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, Iran.","DOI":"10.1109\/IranianCEE.2016.7585572"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s13638-023-02270-3","article-title":"A Positioning Algorithm Based on Improved Robust Extended Kalman Filter with NLOS Identification and Mitigation","volume":"2023","author":"Wang","year":"2023","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_28","unstructured":"(2023, September 06). 5G Channel Model for Bands up to 100 GHz. Available online: https:\/\/prepareforchange.net\/wp-content\/uploads\/2018\/12\/5G_Channel_Model_for_bands_up_to100_GHz2015-12-6.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105257","DOI":"10.1016\/j.infrared.2024.105257","article-title":"PiDiNet-TIR: An Improved Edge Detection Algorithm for Weakly Textured Thermal Infrared Images Based on PiDiNet","volume":"138","author":"Li","year":"2024","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105223","DOI":"10.1016\/j.infrared.2024.105223","article-title":"Deep Soft Threshold Feature Separation Network for Infrared Handprint Identity Recognition and Time Estimation","volume":"138","author":"Yu","year":"2024","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"124551","DOI":"10.1016\/j.eswa.2024.124551","article-title":"Multi-Task Learning for Hand Heat Trace Time Estimation and Identity Recognition","volume":"255","author":"Yu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Diao, H., and Zhao, J. (2019, January 20\u201324). CMD-Based NLOS Identification and Mitigation in Wireless Sensor Networks. Proceedings of the 2019 IEEE International Conference on Communications Workshops (ICC Workshops), Shanghai, China.","DOI":"10.1109\/ICCW.2019.8757187"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TSM.2022.3181468","article-title":"One Class Process Anomaly Detection Using Kernel Density Estimation Methods","volume":"35","author":"Lang","year":"2022","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1109\/ACCESS.2017.2677480","article-title":"An Improved NLOS Identification and Mitigation Approach for Target Tracking in Wireless Sensor Networks","volume":"5","author":"Yan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TWC.2017.2763136","article-title":"Downlink Cellular Network Analysis With LOS\/NLOS Propagation and Elevated Base Stations","volume":"17","author":"Atzeni","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.1109\/TWC.2020.2967726","article-title":"Machine Learning-Enabled LOS\/NLOS Identification for MIMO Systems in Dynamic Environments","volume":"19","author":"Huang","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.comnet.2017.04.012","article-title":"Effect of LOS\/NLOS Propagation on 5G Ultra-Dense Networks","volume":"120","author":"Galiotto","year":"2017","journal-title":"Comput. Netw."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, F., Tang, H., and Chen, J. (2023). Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning. Electronics, 12.","DOI":"10.3390\/electronics12071678"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sang, C.L., Steinhagen, B., Homburg, J.D., Adams, M., Hesse, M., and R\u00fcckert, U. (2020). Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods. Appl. Sci., 10.","DOI":"10.3390\/app10113980"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/JSAC.2010.100907","article-title":"NLOS Identification and Mitigation for Localization Based on UWB Experimental Data","volume":"28","author":"Gifford","year":"2010","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wen, H., Markham, A., Trigoni, N., Blunsom, P., and Frolik, J. (2013, January 7\u20139). Identification and Mitigation of Non-Line-of-Sight Conditions Using Received Signal Strength. Proceedings of the 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon, France.","DOI":"10.1109\/WiMOB.2013.6673428"},{"key":"ref_42","first-page":"12","article-title":"NLOS Identification for UWB Body Communications","volume":"124","author":"Tabaa","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1109\/TWC.2014.2372341","article-title":"Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength","volume":"14","author":"Xiao","year":"2015","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wen, K., Yu, K., and Li, Y. (September, January 28). NLOS Identification and Compensation for UWB Ranging Based on Obstruction Classification. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081702"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stahlke, M., Kram, S., Mumme, T., and Seitz, J. (2019, January 4\u20136). Discrete Positioning Using UWB Channel Impulse Responses and Machine Learning. Proceedings of the 2019 International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany.","DOI":"10.1109\/ICL-GNSS.2019.8752853"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Barral, V., Escudero, C.J., Garc\u00eda-Naya, J.A., and Maneiro-Catoira, R. (2019). NLOS Identification and Mitigation Using Low-Cost UWB Devices. Sensors, 19.","DOI":"10.3390\/s19163464"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kram, S., Stahlke, M., Feigl, T., Seitz, J., and Thielecke, J. (2019). UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis. Sensors, 19.","DOI":"10.3390\/s19245547"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chang, T., Jiang, S., Sun, Y., Jia, A., and Wang, W. (2021, January 22\u201326). Multi-Bandwidth NLOS Identification Based on Deep Learning Method. Proceedings of the 2021 15th European Conference on Antennas and Propagation (EuCAP), Dusseldorf, Germany.","DOI":"10.23919\/EuCAP51087.2021.9411236"},{"key":"ref_49","unstructured":"Ramadan, M., Sark, V., Gutierrez, J., and Grass, E. (2018, January 14\u201316). NLOS Identification for Indoor Localization Using Random Forest Algorithm. Proceedings of the 22nd International ITG Workshop on Smart Antennas, Bochum, Germany."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1109\/LAWP.2019.2934466","article-title":"Applying Random Forest and Multipath Fingerprints to Enhance TDOA Localization Systems","volume":"18","year":"2019","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kurniawan, E., Zhiwei, L., and Sun, S. (2017, January 4\u20138). Machine Learning-Based Channel Classification and Its Application to IEEE 802.11ad Communications. Proceedings of the 2017 IEEE Global Communications Conference, Singapore.","DOI":"10.1109\/GLOCOM.2017.8254052"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1109\/LCOMM.2019.2940023","article-title":"NLOS Identification via AdaBoost for Wireless Network Localization","volume":"23","author":"Zhu","year":"2019","journal-title":"IEEE Commun. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chitambira, B., Armour, S., Wales, S., and Beach, M. (2017, January 19\u201322). NLOS Identification and Mitigation for Geolocation Using Least-Squares Support Vector Machines. Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925566"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.aeue.2018.02.003","article-title":"NLOS Identification for UWB Localization Based on Import Vector Machine","volume":"87","author":"Yang","year":"2018","journal-title":"AEU J. Electron. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Krishnan, S., Santos, R.X.M., Ranier Yap, E., and Zin, M.T. (2018, January 18\u201321). Improving UWB Based Indoor Positioning in Industrial Environments through Machine Learning. Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2018.8581305"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Barral, V., Escudero, C.J., and Garc\u00eda-Naya, J.A. (2019, January 2\u20136). NLOS Classification Based on RSS and Ranging Statistics Obtained from Low-Cost UWB Devices. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902949"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/LCOMM.2020.3039251","article-title":"LOS\/NLOS Identification for Indoor UWB Positioning Based on Morlet Wavelet Transform and Convolutional Neural Networks","volume":"25","author":"Cui","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-H., Nguyen, M.-T., Choi, J., and Kim, Y.-H. (2018). NLOS Identification in WLANs Using Deep LSTM with CNN Features. Sensors, 18.","DOI":"10.3390\/s18114057"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1109\/LCOMM.2018.2872522","article-title":"CNN-Based LOS\/NLOS Identification in 3-D Massive MIMO Systems","volume":"22","author":"Zeng","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/LCOMM.2023.3265272","article-title":"A Simple Efficient Lightweight CNN Method for LOS\/NLOS Identification in Wireless Communication Systems","volume":"27","author":"Zhu","year":"2023","journal-title":"IEEE Commun. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/LCOMM.2023.3260953","article-title":"A Lightweight CIR-Based CNN With MLP for NLOS\/LOS Identification in a UWB Positioning System","volume":"27","author":"Si","year":"2023","journal-title":"IEEE Commun. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"144705","DOI":"10.1109\/ACCESS.2023.3344640","article-title":"NLOS Identification for UWB Positioning Based on IDBO and Convolutional Neural Networks","volume":"11","author":"Kong","year":"2023","journal-title":"IEEE Access"},{"key":"ref_63","unstructured":"Chalapathy, R., Menon, A.K., and Chawla, S. (2018). Anomaly Detection Using One-Class Neural Networks 2019. arXiv."},{"key":"ref_64","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., M\u00fcller, E., and Kloft, M. (2018, January 10\u201315). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2013.09.055","article-title":"Autoencoder for Words","volume":"139","author":"Liou","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhai, J., Zhang, S., Chen, J., and He, Q. (2018, January 7\u201310). Autoencoder and Its Various Variants. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00080"},{"key":"ref_67","first-page":"103788","article-title":"GFSegNet: A Multi-Scale Segmentation Model for Mining Area Ground Fissures","volume":"128","author":"Chen","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Cao, V.L., Nicolau, M., and McDermott, J. (2016, January 17\u201321). A Hybrid Autoencoder and Density Estimation Model for Anomaly Detection. Proceedings of the Parallel Problem Solving from Nature\u2014PPSN XIV, Edinburgh, UK.","DOI":"10.1007\/978-3-319-45823-6_67"},{"key":"ref_69","unstructured":"Dotti, D., Popa, M., and Asteriadis, S. (March, January 27). Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments. Proceedings of the International Conference on Computer Vision Theory and Applications, Porto, Portugal."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Abati, D., Porrello, A., Calderara, S., and Cucchiara, R. (2019, January 15\u201320). Latent Space Autoregression for Novelty Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00057"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., and Breckon, T.P. (2018, January 2\u20136). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. Proceedings of the Computer Vision\u2014ACCV 2018, Perth, Australia.","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref_72","first-page":"103115","article-title":"A Deep Encoder-Decoder Network for Anomaly Detection in Driving Trajectory Behavior under Spatio-Temporal Context","volume":"115","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1109\/TWC.2021.3060482","article-title":"Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems","volume":"20","author":"Wu","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1587\/transcom.2015EBI0002","article-title":"Massive MIMO Technologies and Challenges towards 5G","volume":"E99-B","author":"Papadopoulos","year":"2016","journal-title":"IEICE Trans. Commun."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/JSAC.2023.3273768","article-title":"Massive MIMO Evolution Toward 3GPP Release 18","volume":"41","author":"Jin","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_77","first-page":"103871","article-title":"Application of an Improved U-Net with Image-to-Image Translation and Transfer Learning in Peach Orchard Segmentation","volume":"130","author":"Cheng","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_78","first-page":"103141","article-title":"DBFGAN: Dual Branch Feature Guided Aggregation Network for Remote Sensing Image","volume":"116","author":"Chu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_79","first-page":"103052","article-title":"Advanced Wildfire Detection Using Generative Adversarial Network-Based Augmented Datasets and Weakly Supervised Object Localization","volume":"114","author":"Park","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_80","unstructured":"(2023, November 10). 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; User Equipment (UE) Radio Transmission and Reception; Part 1: Range 1 Standalone. Available online: https:\/\/portal.3gpp.org\/desktopmodules\/Specifications\/SpecificationDetails.aspx?specificationId=3283."},{"key":"ref_81","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6494\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:09:56Z","timestamp":1760112596000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6494"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"references-count":81,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196494"],"URL":"https:\/\/doi.org\/10.3390\/s24196494","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,9]]}}}