{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:21:42Z","timestamp":1775020902855,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62127801"],"award-info":[{"award-number":["62127801"]}]},{"name":"National Natural Science Foundation of China","award":["61971257"],"award-info":[{"award-number":["61971257"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFD0901000"],"award-info":[{"award-number":["2020YFD0901000"]}]},{"name":"National Natural Science Foundation of China","award":["LZC0020"],"award-info":[{"award-number":["LZC0020"]}]},{"name":"National Natural Science Foundation of China","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"name":"National Key R&amp;D Program of China","award":["62127801"],"award-info":[{"award-number":["62127801"]}]},{"name":"National Key R&amp;D Program of China","award":["61971257"],"award-info":[{"award-number":["61971257"]}]},{"name":"National Key R&amp;D Program of China","award":["2020YFD0901000"],"award-info":[{"award-number":["2020YFD0901000"]}]},{"name":"National Key R&amp;D Program of China","award":["LZC0020"],"award-info":[{"award-number":["LZC0020"]}]},{"name":"National Key R&amp;D Program of China","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"name":"Peng Cheng Laboratory","award":["62127801"],"award-info":[{"award-number":["62127801"]}]},{"name":"Peng Cheng Laboratory","award":["61971257"],"award-info":[{"award-number":["61971257"]}]},{"name":"Peng Cheng Laboratory","award":["2020YFD0901000"],"award-info":[{"award-number":["2020YFD0901000"]}]},{"name":"Peng Cheng Laboratory","award":["LZC0020"],"award-info":[{"award-number":["LZC0020"]}]},{"name":"Peng Cheng Laboratory","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"name":"CAST","award":["62127801"],"award-info":[{"award-number":["62127801"]}]},{"name":"CAST","award":["61971257"],"award-info":[{"award-number":["61971257"]}]},{"name":"CAST","award":["2020YFD0901000"],"award-info":[{"award-number":["2020YFD0901000"]}]},{"name":"CAST","award":["LZC0020"],"award-info":[{"award-number":["LZC0020"]}]},{"name":"CAST","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The space\u2013air\u2013ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.<\/jats:p>","DOI":"10.3390\/s23229207","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T08:19:43Z","timestamp":1700122783000},"page":"9207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comprehensive Simulation Framework for Space\u2013Air\u2013Ground Integrated Network Propagation Channel Research"],"prefix":"10.3390","volume":"23","author":[{"given":"Zekai","family":"Zhang","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoyang","family":"Song","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314000, China"},{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingzehua","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3963-9739","authenticated-orcid":false,"given":"Ziyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangwang","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Men","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39824","DOI":"10.1109\/ACCESS.2023.3269297","article-title":"Innovative trends in the 6G era: A comprehensive survey of architecture, applications, technologies, and challenges","volume":"11","author":"Khanh","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/MNET.121.2100324","article-title":"Edge intelligence for mission-critical 6G services in space-air-ground integrated networks","volume":"36","author":"Hou","year":"2022","journal-title":"IEEE Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/MVT.2020.3019650","article-title":"Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable\/low-latency service","volume":"15","author":"Du","year":"2020","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_4","first-page":"100867","article-title":"An efficient edge computing management mechanism for sustainable smart cities","volume":"38","author":"Nguyen","year":"2023","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Quy, N.M., Ngoc, L.A., Ban, N.T., Hau, N.V., and Quy, V.K. (Wirel. Pers. Commun., 2023). Edge computing for real-time Internet of Things applications: Future internet revolution, Wirel. Pers. Commun., in press.","DOI":"10.1007\/s11277-023-10669-w"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1109\/TAP.2022.3149665","article-title":"Artificial intelligence enabled radio propagation for communications\u2014Part II: Scenario identification and channel modeling","volume":"70","author":"Huang","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/JSAC.2023.3242727","article-title":"Gradient and channel aware dynamic scheduling for over-the-air computation in federated edge learning systems","volume":"41","author":"Du","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1109\/TNET.2022.3152150","article-title":"SDN-based resource allocation in edge and cloud computing systems: An evolutionary Stackelberg differential game approach","volume":"30","author":"Du","year":"2022","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5850","DOI":"10.1109\/TVT.2015.2473687","article-title":"Vehicle-to-vehicle radio channel characterization in crossroad scenarios","volume":"65","author":"He","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, R., Renaudin, O., Bas, C.U., Sangodoyin, S., and Molisch, A.F. (2017, January 8\u201313). Vehicle-to-vehicle propagation channel for truck-to-truck and mixed passenger freight convoy. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292782"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47797","DOI":"10.1109\/ACCESS.2020.2979220","article-title":"Wireless channel propagation scenarios identification: A perspective of machine learning","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"6052","DOI":"10.1109\/TAP.2021.3069491","article-title":"Multilayer machine learning-assisted optimization-based robust design and its applications to antennas and array","volume":"69","author":"Wu","year":"2021","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/TAP.2019.2963570","article-title":"Multistage collaborative machine learning and its application to antenna modeling and optimization","volume":"68","author":"Wu","year":"2020","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/MWC.001.1900072","article-title":"A comprehensive simulation platform for space-air-ground integrated network","volume":"27","author":"Cheng","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3242","DOI":"10.1109\/TAP.2014.2310220","article-title":"QuaDRiGa: A 3-D multi-cell channel model with time evolution for enabling virtual field trials","volume":"62","author":"Jaeckel","year":"2014","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s11277-017-5096-0","article-title":"Scenario classification of wireless network optimization based on big data technology","volume":"102","author":"Yang","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1109\/LAWP.2018.2869548","article-title":"Classification of indoor environments for IoT applications: A machine learning approach","volume":"17","author":"AlHajri","year":"2018","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/TCCN.2017.2741468","article-title":"A machine-learning-based connectivity model for complex terrain large-scale low-power wireless deployments","volume":"3","author":"Oroza","year":"2017","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8489326","DOI":"10.1155\/2018\/8489326","article-title":"Air-to-air path loss prediction based on machine learning methods in urban environments","volume":"2018","author":"Zhang","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xiao, F., Guo, Z., Zhu, H., Xie, X., and Wang, R. (2017, January 21\u201325). AmpN: Real-time LOS\/NLOS identification with WiFi. Proceedings of the 2017 IEEE International Conference on Communications, Paris, France.","DOI":"10.1109\/ICC.2017.7997068"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1109\/LWC.2020.2994945","article-title":"Channel non-line-of-sight identification based on convolutional neural networks","volume":"9","author":"Zheng","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kalakh, M., Kandil, N., and Hakem, N. (2012, January 6\u20139). Neural networks model of an UWB channel path loss in a mine environment. Proceedings of the 2012 IEEE 75th Vehicular Technology Conference, Yokohama, Japan.","DOI":"10.1109\/VETECS.2012.6240318"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1109\/LAWP.2019.2932904","article-title":"Prediction of channel excess attenuation for satellite communication systems at Q-band using artificial neural network","volume":"18","author":"Bai","year":"2019","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14225","DOI":"10.1109\/TVT.2020.3037212","article-title":"A novel atmosphere-informed data-driven predictive channel modeling for B5G\/6G satellite-terrestrial wireless communication systems at Q-band","volume":"69","author":"Bai","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/JIOT.2017.2739181","article-title":"Narrowband internet of things: Simulation and modeling","volume":"5","author":"Miao","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/TMC.2017.2690636","article-title":"Improving VANET simulation with calibrated vehicular mobility traces","volume":"16","author":"Celes","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.1109\/JSAC.2016.2605239","article-title":"Hypergraph-based wireless distributed storage optimization for cellular D2D underlays","volume":"34","author":"Wang","year":"2016","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Panigrahi, B., Rath, H.K., Misra, P., and Simha, A. (2018, January 15\u201319). LTE-CAS: LTE-based criticality aware scheduling for UAV assisted emergency response. Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops, Honolulu, HI, USA.","DOI":"10.1109\/INFCOMW.2018.8406949"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/MNET.2017.1700206","article-title":"Drone assisted vehicular networks: Architecture, challenges and opportunities","volume":"32","author":"Shi","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hou, X., Wang, J., Jiang, C., Zhang, X., Ren, Y., and Debbah, M. (IEEE Trans. Wirel. Commun., 2023). UAV-enabled covert federated learning, IEEE Trans. Wirel. Commun., in press.","DOI":"10.1109\/TWC.2023.3245621"},{"key":"ref_32","first-page":"87","article-title":"Dynamic modeling and simulation of multi-scenario satellite communication channels","volume":"38","author":"He","year":"2023","journal-title":"Chin. J. Radio Sci."},{"key":"ref_33","unstructured":"Molisch, A.F. (2012). Wireless Communications, John Wiley & Sons."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"217526","DOI":"10.1109\/ACCESS.2020.3039410","article-title":"Short-term traffic flow prediction using the modified elman recurrent neural network optimized through a genetic algorithm","volume":"8","author":"Mirshafiei","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:23:48Z","timestamp":1760131428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,16]]},"references-count":34,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229207"],"URL":"https:\/\/doi.org\/10.3390\/s23229207","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,16]]}}}