{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T13:56:32Z","timestamp":1762091792241,"version":"build-2065373602"},"reference-count":21,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2023,12,1]]},"DOI":"10.1587\/transcom.2023cep0006","type":"journal-article","created":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T22:27:51Z","timestamp":1694384871000},"page":"1350-1362","source":"Crossref","is-referenced-by-count":1,"title":["Deep Neural Networks Based End-to-End DOA Estimation System"],"prefix":"10.23919","volume":"E106.B","author":[{"given":"Daniel Akira","family":"ANDO","sequence":"first","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuya","family":"KASE","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshihiko","family":"NISHIMURA","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takanori","family":"SATO","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeo","family":"OHGANE","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasutaka","family":"OGAWA","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junichiro","family":"HAGIWARA","sequence":"additional","affiliation":[{"name":"Graduate School\/Faculty of Information Science & Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","unstructured":"[1] National Institute of Information and Communications Technology (NICT), Beyond 5G\/6G White Paper, June 2022."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] R.O. Schmidt, \u201cMultiple emitter location and signal parameter estimation,\u201d IEEE Trans. Antennas Propag., vol.AP-34, no.3, pp.276-280, March 1986, DOI: 10.1109\/TAP.1986.1143830. 10.1109\/tap.1986.1143830","DOI":"10.1109\/TAP.1986.1143830"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] B.D. Rao and K.V. S. Hari, \u201cPerformance analysis of root-MUSIC,\u201d IEEE Trans. Acoust., Speech, Signal Process., vol.37, no.12, pp.1939-1949, Dec. 1989, DOI: 10.1109\/29.45540. 10.1109\/29.45540","DOI":"10.1109\/29.45540"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] K. Hayashi, M. Nagahara, and T. Tanaka, \u201cA user&apos;s guide to compressed sensing for communications systems,\u201d IEICE Trans. Commun., vol.E96-B, no.3, pp.685-712, March 2013, DOI: 10.1587\/transcom.E96.B.685. 10.1587\/transcom.e96.b.685","DOI":"10.1587\/transcom.E96.B.685"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] T. Nishimura, Y. Ogawa, T. Ohgane, and J. Hagiwara, \u201cRadio techniques incorporating sparse modeling,\u201d IEICE Trans. Fundamentals, vol.E104-A, no.3, pp.591-603, March 2021, DOI: 10.1587\/transfun.2020EAI0001. 10.1587\/transfun.2020eai0001","DOI":"10.1587\/transfun.2020EAI0001"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] M. \u00c7etin, D.M. Malioutov, and A.S. Willsky, \u201cA variational technique for source localization based on a sparse signal reconstruction perspective,\u201d Proc. IEEE ICASSP 2002, pp.III-2965-III-2968, May 2002, DOI: 10.1109\/ICASSP.2002.5745271. 10.1109\/ICASSP.2002.5745271","DOI":"10.1109\/ICASSP.2002.5745271"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] T. O&apos;Shea and J. Hoydis, \u201cAn introduction to deep learning for the physical layer,\u201d IEEE Trans. Cogn. Commun. Netw., vol.3, no.4, pp.563-575, Dec. 2017, DOI: 10.1109\/TCCN.2017.2758370. 10.1109\/tccn.2017.2758370","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Huang, J. Yang, H. Huang, Y. Song, and G. Gui, \u201cDeep learning for super-resolution channel estimation and DOA estimation based massive MIMO system,\u201d IEEE Trans. Veh. Technol., vol.67, no.9, pp.8549-8560, Sept. 2018, DOI: 10.1109\/TVT.2018.2851783. 10.1109\/tvt.2018.2851783","DOI":"10.1109\/TVT.2018.2851783"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] M. Chen, Y. Gong, and X. Mao, \u201cDeep neural network for estimation of direction of arrival with antenna array,\u201d IEEE Access, vol.8, pp.140688-140698, Aug. 2020, DOI: 10.1109\/ACCESS.2020.3012582. 10.1109\/access.2020.3012582","DOI":"10.1109\/ACCESS.2020.3012582"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] D. Hu, Y. Zhang, L. He, and J. Wu, \u201cLow-complexity deep-learning-based DOA estimation for hybrid massive MIMO systems with uniform circular arrays,\u201d IEEE Wireless Commun. Lett., vol.9, no.1, pp.83-86, Jan. 2020, DOI: 10.1109\/LWC.2019.2942595. 10.1109\/lwc.2019.2942595","DOI":"10.1109\/LWC.2019.2942595"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Z.-M. Liu, C. Zhang, and P.S. Yu, \u201cDirection-of-arrival estimation based on deep neural networks with robustness to array imperfections,\u201d IEEE Trans. Antennas Propag., vol.66, no.12, pp.7315-7327, Dec. 2018, DOI: 10.1109\/TAP.2018.2874430. 10.1109\/tap.2018.2874430","DOI":"10.1109\/TAP.2018.2874430"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S. Zhang, S. Zhang, T. Huang, and W. Gao, \u201cSpeech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching,\u201d IEEE Trans. Multimedia, vol.20, no.6, pp.1576-1590, June 2018, DOI: 10.1109\/TMM.2017.2766843. 10.1109\/tmm.2017.2766843","DOI":"10.1109\/TMM.2017.2766843"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[14] S. Ju, Y. Xing, O. Kanhere, and T.S. Rappaport, \u201cMillimeter wave and sub-terahertz spatial statistical channel model for an indoor office building,\u201d IEEE J. Sel. Areas Commun., vol.39, no.6, pp.1561-1575, June 2021, DOI: 10.1109\/JSAC.2021.3071844. 10.1109\/jsac.2021.3071844","DOI":"10.1109\/JSAC.2021.3071844"},{"key":"14","unstructured":"[15] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, R. Jozefowicz, Y. Jia, L. Kaiser, M. Kudlur, J. Levenberg, D. Man\u00e9, M. Schuster, R. Monga, S. Moore, D. Murray, C. Olah, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi\u00e9gas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, \u201cTensorFlow: large-scale machine learning on heterogeneous systems,\u201d Software available from tensorflow.org, 2015."},{"key":"15","doi-asserted-by":"publisher","unstructured":"[16] Y. Kase, T. Nishimura, T. Ohgane, Y. Ogawa, D. Kitayama, and Y. Kishiyama, \u201cFundamental trial on DOA estimation with deep learning,\u201d IEICE Trans. Commun., vol.E103-B, no.10, pp.1127-1135, Oct. 2020, DOI: 10.1587\/transcom.2019EBP3260. 10.1587\/transcom.2019ebp3260","DOI":"10.1587\/transcom.2019EBP3260"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[17] Y. Kase, T. Nishimura, T. Ohgane, Y. Ogawa, T. Sato, and Y. Kishiyama, \u201cAccuracy improvement in DOA estimation with deep learning,\u201d IEICE Trans. Commun., vol.E105-B, no.5, pp. 588-599, May 2022, DOI: 10.1587\/transcom.2021EBT0001. 10.1587\/transcom.2021ebt0001","DOI":"10.1587\/transcom.2021EBT0001"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[18] D.A. Ando, T. Nishimura, T. Sato, T. Ohgane, Y. Ogawa, and J. Hagiwara, \u201cA proposal of an end-to-end DoA estimation system aided by deep learning,\u201d WPMC 2022, pp.98-103, Oct. 2022, DOI: 10.1109\/WPMC55625.2022.10014749. 10.1109\/wpmc55625.2022.10014749","DOI":"10.1109\/WPMC55625.2022.10014749"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[19] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer New York, 2013. 10.1007\/978-1-0716-1418-1","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] R.T. Nakatsu, \u201cAn evaluation of four resampling methods used in machine learning classification,\u201d IEEE Intell. Syst., vol.36, no.3, pp.51-57, June 2021, DOI: 10.1109\/MIS.2020.2978066. 10.1109\/mis.2020.2978066","DOI":"10.1109\/MIS.2020.2978066"},{"key":"20","unstructured":"[21] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d arXiv:1502.03167v3, March 2015. 10.48550\/arXiv.1502.03167"},{"key":"21","unstructured":"[22] D.P. Kingma and J.L. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv:1412.6980v9, Jan. 2017. 10.48550\/arXiv.1412.6980"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/12\/E106.B_2023CEP0006\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:02:30Z","timestamp":1704898950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/12\/E106.B_2023CEP0006\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":21,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2023cep0006","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"type":"print","value":"0916-8516"},{"type":"electronic","value":"1745-1345"}],"subject":[],"published":{"date-parts":[[2023,12,1]]},"article-number":"2023CEP0006"}}