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Sen. Netw."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practical scenarios. Here, we reformulate the direction finding or angle-of-arrival (AoA) estimation problem as an image recovery task of the spatial spectrum and propose a new model-driven deep neural network (MoD-DNN) framework. The proposed MoD-DNN scheme comprises three modules: a multi-task autoencoder-based beamformer, a coarray spectrum generation module, and a model-driven deep learning-based spatial spectrum reconstruction module. Our technique enables automatic calibration of angular-dependent phase error, thereby enhancing the resilience of direction-finding precision against realistic system non-idealities. We validate the proposed scheme both using numerical simulations and field tests. The results show that the proposed MoD-DNN framework enables effective spectrum calibration and accurate AoA estimation. To the best of our knowledge, this study marks the first successful demonstration of hybrid data-and-model-driven direction finding utilizing readily available commodity 5G gNodeB.<\/jats:p>","DOI":"10.1145\/3712305","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:25:39Z","timestamp":1736853939000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Model-Driven Deep Neural Network for Enhancing Direction Finding with Commodity 5G gNodeB"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6579-9798","authenticated-orcid":false,"given":"Shengheng","family":"Liu","sequence":"first","affiliation":[{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China and Purple Mountain Laboratories, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0661-2281","authenticated-orcid":false,"given":"Zihuan","family":"Mao","sequence":"additional","affiliation":[{"name":"ZTE Corporation, Nanjing, China and School of Information Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1162-8349","authenticated-orcid":false,"given":"Xingkang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5835-4539","authenticated-orcid":false,"given":"Mengguan","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Anhui University, Hefei, China and Purple Mountain Laboratories, Nanjing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8694-7091","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Purple Mountain Laboratories, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3616-4616","authenticated-orcid":false,"given":"Yongming","family":"Huang","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China and Purple Mountain Laboratories, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0809-8511","authenticated-orcid":false,"given":"Xiaohu","family":"You","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Southeast University, Nanjing, China and Purple Mountain Laboratories, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MWC.004.2200482","article-title":"Toward 6G TK \\(\\upmu\\)  extreme connectivity: Architecture, key technologies and experiments","volume":"30","author":"You Xiaohu","year":"2023","unstructured":"Xiaohu You, Yongming Huang, Shengheng Liu, Dongming Wang, Junchao Ma, Chuan Zhang, Hang Zhan, Cheng Zhang, Jiao Zhang, Zening Liu et\u00a0al. 2023. 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