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Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two\u2010view distributed cooperative DeepJSCC (two\u2010view\u2010DC\u2010DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two\u2010view\u2010DC\u2010DeepJSCC with information disentanglement (two\u2010view\u2010DC\u2010DeepJSCC\u2010D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two\u2010view\u2010DC\u2010DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two\u2010view\u2010DC\u2010DeepJSCC\u2010D effectively captures both common and private information from two\u2010view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two\u2010view\u2010DC\u2010DeepJSCC\u2010D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two\u2010view\u2010DC\u2010DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two\u2010view\u2010DC\u2010DeepJSCC\u2010D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two\u2010view\u2010DC\u2010DeepJSCC\u2010D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two\u2010view\u2010DC\u2010DeepJSCC\u2010D exhibits stronger robustness across various signal\u2010to\u2010noise ratios.<\/jats:p>","DOI":"10.1155\/int\/5081017","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T08:29:48Z","timestamp":1733905788000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Two\u2010View Image Semantic Cooperative Nonorthogonal Transmission in Distributed Edge Networks"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8386-8038","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-135X","authenticated-orcid":false,"given":"Donghong","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3415-7682","authenticated-orcid":false,"given":"Zhicheng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8637-852X","authenticated-orcid":false,"given":"Lisu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4104-5136","authenticated-orcid":false,"given":"Yanqing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-2177","authenticated-orcid":false,"given":"Zhiquan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/103085.103089"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/30.920468"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcomm.2005.852852"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2003.810631"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcomm.2009.0901.070075"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2017.2674667"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsac.2022.3221963"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsait.2020.2987203"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2021.3090048"},{"key":"e_1_2_10_10_2","article-title":"Deepjsccq: Constellation Constrained Deep Joint Source-Channel Coding","volume":"10","author":"Tung T.-Y.","year":"2022","journal-title":"IEEE Journal on Selected Areas in Information Theory"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsac.2020.3036955"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2023.3234408"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsac.2023.3288238"},{"key":"e_1_2_10_14_2","unstructured":"ZhangX. 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