{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T23:04:08Z","timestamp":1780009448633,"version":"3.53.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92367201"],"award-info":[{"award-number":["92367201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406026"],"award-info":[{"award-number":["62406026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2024YFC3306900"],"award-info":[{"award-number":["2024YFC3306900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2026,6,1]]},"DOI":"10.1109\/jiot.2026.3669156","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T20:57:02Z","timestamp":1772485022000},"page":"23264-23277","source":"Crossref","is-referenced-by-count":0,"title":["Fast-Tactical Diffusion for On-Board AAV Spectrum-Level Signal Deception"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4762-8071","authenticated-orcid":false,"given":"Lin","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5512-1450","authenticated-orcid":false,"given":"Yihan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-2887","authenticated-orcid":false,"given":"Zeyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyun","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3537-6414","authenticated-orcid":false,"given":"Chao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2559-045X","authenticated-orcid":false,"given":"Fangxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science and Engineering and Future Network of Intelligence Institute, The Chinese University of Hongkong, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6441-9711","authenticated-orcid":false,"given":"Jianping","family":"An","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2666439"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s23156976"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2025.3639401"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2025.103127"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref6","article-title":"Diffusion models for wireless communications","author":"Letafati","year":"2023","journal-title":"arXiv:2310.07312"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.2300310"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3574284"},{"key":"ref9","article-title":"Diffusion models for future networks and communications: A comprehensive survey","author":"Luong","year":"2025","journal-title":"arXiv:2508.01586"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3649348"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM54140.2023.10436771"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s12243-023-00980-9"},{"key":"ref13","article-title":"Diffusion models on the edge: Challenges, optimizations, and applications","author":"Zheng","year":"2025","journal-title":"arXiv:2504.15298"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2025.3534463"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2024.3353265"},{"key":"ref16","first-page":"1747","article-title":"Pixel recurrent neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Van Den Oord"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.21437\/SSW.2016"},{"key":"ref18","first-page":"3918","article-title":"Parallel WaveNet: Fast high-fidelity speech synthesis","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Oord"},{"key":"ref19","first-page":"1","article-title":"Learning structured output representation using deep conditional generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Sohn"},{"key":"ref20","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref21","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref22","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020","journal-title":"arXiv:2010.02502"},{"issue":"6","key":"ref23","first-page":"2320","article-title":"A review on software defined radio (SDR) in communication","volume":"12","author":"Goyal","year":"2024","journal-title":"Int. J. Creative Res. Thoughts"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICSP62122.2024.10743556"},{"key":"ref25","article-title":"Radio generation using generative adversarial networks with an unrolled design","author":"Wang","year":"2023","journal-title":"arXiv:2306.13893"},{"key":"ref26","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv:1411.1784"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2025.3594547"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3487627"},{"key":"ref29","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Dhariwal"},{"key":"ref30","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2797022"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/rs16234550"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-020-00809-z"},{"key":"ref34","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"ref36","article-title":"T-CGAN: Conditional generative adversarial network for data augmentation in noisy time series with irregular sampling","author":"Ramponi","year":"2018","journal-title":"arXiv:1811.08295"},{"key":"ref37","first-page":"1","article-title":"Neural discrete representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Van Den Oord"},{"key":"ref38","first-page":"1","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Heusel"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref41","volume-title":"5G; NR; N (BS) Radio Transmission and Reception (Release 16)","year":"2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11534158\/11417839.pdf?arnumber=11417839","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T22:40:21Z","timestamp":1780008021000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11417839\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":42,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2026.3669156","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}