{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:58:42Z","timestamp":1775890722814,"version":"3.50.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2023,2,1]]},"DOI":"10.1587\/transcom.2022cei0002","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T22:12:28Z","timestamp":1660083148000},"page":"75-83","source":"Crossref","is-referenced-by-count":39,"title":["Machine Learning in 6G Wireless Communications"],"prefix":"10.23919","volume":"E106.B","author":[{"given":"Tomoaki","family":"OHTSUKI","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Keio University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","unstructured":"[1] NTT DOCOMO, \u201cWhite Paper: 5G Evolution and 6G (Version 4.0),\u201d Jan. 2022. https:\/\/www.docomo.ne.jp\/english\/binary\/pdf\/corporate\/technology\/whitepaper_6g\/DOCOMO_6G_White_PaperEN_v4.0.pdf"},{"key":"2","unstructured":"[2] The Ministry of Internal Affairs and Communications (MIC), \u201cBeyond 5G Promotion Strategy \u2014 Roadmap towards 6G \u2014 ,\u201d June 2020. https:\/\/www.soumu.go.jp\/main_sosiki\/joho_tsusin\/eng\/presentation\/pdf\/Beyond_5G_Promotion_Strategy-Roadmap_towards_6G-.pdf"},{"key":"3","unstructured":"[3] National Institute of Information and Communications Technology (NICT), \u201cBeyond 5G\/6G White Paper (English Version 2.0),\u201d June 2022. https:\/\/beyond5g.nict.go.jp\/images\/download\/NICT_B5G6G_WhitePaperEN_v2_0.pdf"},{"key":"4","unstructured":"[4] KDDI Coorporation, KDDI Research, Inc., \u201cBeyond 5G\/6G White Paper (Version 2.0.1),\u201d Oct. 2021. https:\/\/www.kddi-research.jp\/sites\/default\/files\/kddi_whitepaper_en\/pdf\/KDDI_B5G6G_WhitePaperEN_2.0.1.pdf"},{"key":"5","unstructured":"[5] NOKIA Bell Labs, \u201cCommunications in the 6G Era,\u201d Sept. 2020. https:\/\/d1p0gxnqcu0lvz.cloudfront.net\/documents\/Asset_20200909220306.pdf"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] H. Viswanathan and P.E. Mogensen, \u201cCommunications in the 6G Era,\u201d IEEE Access, vol.8, pp.57063-57074, 2020. 10.1109\/access.2020.2981745","DOI":"10.1109\/ACCESS.2020.2981745"},{"key":"7","unstructured":"[7] Ericcson: \u201c6G-Connecting a cyber-physical world,\u201d Feb. 2022. https:\/\/www.ericsson.com\/4927de\/assets\/local\/reports-papers\/white-papers\/6g-connecting-a-cyber-physical-world.pdf"},{"key":"8","unstructured":"[8] 6G Flagship, University of Oulu, \u201c6G Visions on paper,\u201d https:\/\/www.6gflagship.com\/white-papers\/"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] J. Du, C. Jiang, J. Wang, Y. Ren, and M. Debbah, \u201cMachine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable\/low-latency service,\u201d IEEE Veh. Technol. Mag., vol.15, no.4, pp.122-134, Dec. 2020. 10.1109\/mvt.2020.3019650","DOI":"10.1109\/MVT.2020.3019650"},{"key":"10","unstructured":"[10] 3GPP TR 23.791, \u201cStudy of Enablers for Network Automation for 5G,\u201d June 2019."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] T. O'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. 10.1109\/tccn.2017.2758370","DOI":"10.1109\/TCCN.2017.2758370"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] X. Tan, W. Xu, K. Sun, Y. Xu, Y. Be'ery, X. You, and C. Zhang, \u201cImproving massive MIMO message passing detectors with deep neural network,\u201d IEEE Trans. Veh. Technol., vol.69, no.2, pp.1267-1280, Feb. 2020. 10.1109\/tvt.2019.2960763","DOI":"10.1109\/TVT.2019.2960763"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] J. Tachibana and T. Ohtsuki, \u201cLearning and analysis of damping factor in massive MIMO detection using BP algorithm with node selection,\u201d IEEE Access, vol.8, pp.96859-96866, 2020. 10.1109\/access.2020.2995171","DOI":"10.1109\/ACCESS.2020.2995171"},{"key":"14","unstructured":"[14] J.R. Hershey, J.L. Roux, and F. Weninger, \u201cDeep unfolding: Model-based inspiration of novel deep architectures,\u201d arXiv:1409.2574v4, 2014. 10.48550\/arXiv.1409.2574"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] V. Lempitsky, A. Vedaldi, and D. Ulyanov, \u201cDeep image prior,\u201d Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., pp.9446-9454, June 2018. 10.1109\/cvpr.2018.00984","DOI":"10.1109\/CVPR.2018.00984"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] H. Hirose, T. Ohtsuki, and G. Gui, \u201cDeep learning-based channel estimation for massive MIMO systems with pilot contamination,\u201d IEEE Open J. Veh. Technol., vol.2, pp.67-77, 2021. 10.1109\/ojvt.2020.3045470","DOI":"10.1109\/OJVT.2020.3045470"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] J. Zeng, J. Sun, G. Gui, B. Adebisi, T. Ohtsuki, H. Gacanin, and H. Sari, \u201cDownlink CSI feedback algorithm with deep transfer learning for FDD massive MIMO system,\u201d IEEE Trans. Cogn. Commun. Netw, vol.7, no.4, pp.1253-1265, Dec. 2021. 10.1109\/TCCN.2021.3084409","DOI":"10.1109\/TCCN.2021.3084409"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] H. Echigo, Y. Cao, M. Bouazizi, and T. Ohtsuki, \u201cA deep learning-based low overhead beam selection in mmWave communications,\u201d IEEE Trans. Veh. Technol., vol.70, no.1, pp.682-691, Jan. 2021. 10.1109\/tvt.2021.3049380","DOI":"10.1109\/TVT.2021.3049380"},{"key":"19","unstructured":"[19] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, MA, USA, 2016."},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] F.A. Aoudia and J. Hoydis, \u201cModel-free training of end-to-end communication systems,\u201d IEEE J. Sel. Areas Commun., vol.37, no.11, pp.2503-2516, Nov. 2019. 10.1109\/jsac.2019.2933891","DOI":"10.1109\/JSAC.2019.2933891"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] W. Fukuda, T. Abiko, T. Nishimura, T. Ohgane, Y. Ogawa, Y. Ohwatari, and Y. Kishiyama, \u201cLow-complexity detection based on belief propagation in a massive MIMO system,\u201d Proc. VTC 2013-Spring, Dresden, Germany, pp.1-5, June 2013. 10.1109\/vtcspring.2013.6692622","DOI":"10.1109\/ISPACS.2013.6704555"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Yang, C. Zhang, X. Liang, S. Xu, and X. You, \u201cImproved symbol based belief propagation detection for large-scale MIMO,\u201d Proc. IEEE Workshop Signal Process. Syst. (SiPS), pp.1-6, Oct. 2015. 10.1109\/sips.2015.7345035","DOI":"10.1109\/SiPS.2015.7345035"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F. Long, T. Lv, R. Cao, and H. Gao, \u201cSingle edge based belief propagation algorithms for MIMO detection,\u201d Proc. IEEE Sarnoff Symposium, Princeton, USA, pp.1-5, May 2011. 10.1109\/sarnof.2011.5876456","DOI":"10.1109\/SARNOF.2011.5876456"},{"key":"24","unstructured":"[24] Y. Gao, H. Niu, and T. Kaiser, \u201cMassive MIMO detection based on belief propagation in spatially correlated channels,\u201d Proc. IEEE 11th Int. ITG Conf. Syst. Commun. Coding, pp.1-6, Feb. 2017."},{"key":"25","unstructured":"[25] T. Takahashi, S. Ibi, T. Ohgane, and S. Sampei, \u201cOn normalized belief of Gaussian BP in correlated large MIMO channels,\u201d 2016 International Symposium on Information Theory and Its Applications (ISITA), pp.458-462, Nov. 2016."},{"key":"26","unstructured":"[26] J. Tachibana and T. Ohtsuki, \u201cMassive MIMO detection with BP algorithm using DIP for removing residual interference and noise and DNN for learning scaling factor,\u201d IEICE Technical Report, RCS2020-253, March 2021 (in Japanese)."},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] H. Yin, D. Gesbert, M. Filippou, and Y. Liu, \u201cA coordinated approach to channel estimation in large-scale multiple-antenna systems,\u201d IEEE J. Sel. Areas Commun., vol.31, no.2, pp.264-273, Feb. 2013. 10.1109\/jsac.2013.130214","DOI":"10.1109\/JSAC.2013.130214"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] 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. 10.1109\/tvt.2018.2851783","DOI":"10.1109\/TVT.2018.2851783"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] S.J. Pan and Q. Yang, \u201cA survey on transfer learning,\u201d IEEE Trans. Knowl. Data Eng., vol.22, no.10, pp.1345-1359, Oct. 2010. 10.1109\/tkde.2009.191","DOI":"10.1109\/TKDE.2009.191"},{"key":"30","unstructured":"[30] 3GPP TR 38.901: \u201cStudy on channel model for frequencies from 0.5 to 100GHz,\u201d 3rd Generation Partnership Project; Technical Specification Group Radio Access Network, 2017."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] M. Inoue and T. Ohtsuki, \u201cEvaluation of source data selection for DTL based CSI feedback method in FDD massive MIMO systems,\u201d IEICE Technical Report, RCS2022-62, June 2022 (in Japanese).","DOI":"10.1109\/CCNC51644.2023.10060176"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] P. Wang, Y. Li, L. Song, and B. Vucetic, \u201cMulti-gigabit millimeter wave wireless communications for 5G: From fixed access to cellular networks,\u201d IEEE Commun. Mag., vol.53, no.1, pp.168-178, Jan. 2015. 10.1109\/mcom.2015.7010531","DOI":"10.1109\/MCOM.2015.7010531"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] L. Wei, Q. Li, and G. Wu, \u201cExhaustive, iterative and hybrid initial access techniques in mmwave communications,\u201d 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp.1-6, 2017. 10.1109\/wcnc.2017.7925666","DOI":"10.1109\/WCNC.2017.7925666"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] M. Giordani, M. Mezzavilla, C.N. Barati, S. Rangan, and M. Zorzi, \u201cComparative analysis of initial access techniques in 5G mmWave cellular networks,\u201d 2016 Annual Conference on Information Science and Systems (CISS), pp.268-273, 2016. 10.1109\/ciss.2016.7460513","DOI":"10.1109\/CISS.2016.7460513"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] C.-H. Lin, W.-C. Kao, S.-Q. Zhan, and T.-S. Lee, \u201cBsnet: A deep learning-based beam selection method for mmWave communications,\u201d 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), pp.1-6, 2019. 10.1109\/vtcfall.2019.8891363","DOI":"10.1109\/VTCFall.2019.8891363"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] M.T. Ribeiro, S. Singh, and C. Guestrin, \u201c\u201cWhy should I trust you?\u201d Explaining the predictions of any classifier,\u201d KDD'16: Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144, Aug. 2016. 10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/2\/E106.B_2022CEI0002\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:00:44Z","timestamp":1704898844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/2\/E106.B_2022CEI0002\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2022cei0002","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]},"article-number":"2022CEI0002"}}