{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:55:24Z","timestamp":1743036924724,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030971236"},{"type":"electronic","value":"9783030971243"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-97124-3_28","type":"book-chapter","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T13:16:56Z","timestamp":1648646216000},"page":"375-389","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effective WiTech Identification Using Deep Transfer Learning with\u00a0SNR as\u00a0an\u00a0Additional Feature"],"prefix":"10.1007","author":[{"given":"Sachin","family":"Nayak","sequence":"first","affiliation":[]},{"given":"Amitesh Singh","family":"Sisodia","sequence":"additional","affiliation":[]},{"given":"Subrahamanya Swamy","family":"Peruru","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"Bitar, N., Muhammad, S., Refai, H.H.: Wireless technology identification using deep convolutional neural networks. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/PIMRC.2017.8292183","DOI":"10.1109\/PIMRC.2017.8292183"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Cabric, D., Tkachenko, A., Brodersen, R.W.: Experimental study of spectrum sensing based on energy detection and network cooperation. In: Proceedings of the First International Workshop on Technology and Policy for Accessing Spectrum, TAPAS \u201906, p. 12-es. Association for Computing Machinery, New York (2006). https:\/\/doi.org\/10.1145\/1234388.1234400","DOI":"10.1145\/1234388.1234400"},{"key":"28_CR3","unstructured":"CRAWDAD a community resource for archiving wireless data at dartmouth. https:\/\/crawdad.org\/about.html. Accessed 13 June 2021"},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/OJVT.2020.2981519","volume":"1","author":"A Dziedzic","year":"2020","unstructured":"Dziedzic, A., Sathya, V., Rochman, M.I., Ghosh, M., Krishnan, S.: Machine learning enabled spectrum sharing in dense LTE-U\/Wi-Fi coexistence scenarios. IEEE Open J. Veh. Technol. 1, 173\u2013189 (2020)","journal-title":"IEEE Open J. Veh. Technol."},{"key":"28_CR5","unstructured":"A new standard for dynamic spectrum sharing. https:\/\/www.ericsson.com\/en\/blog\/2019\/6\/dynamic-spectrum-sharing-standardization. Accessed 15 June 2021"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Kaltenberger, F., Knopp, R., Danneberg, M., Festag, A.: Experimental analysis and simulative validation of dynamic spectrum access for coexistence of 4g and future 5g systems. In: 2015 European Conference on Networks and Communications (EuCNC), pp. 497\u2013501 (2015). https:\/\/doi.org\/10.1109\/EuCNC.2015.7194125","DOI":"10.1109\/EuCNC.2015.7194125"},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TCCN.2019.2899871","volume":"5","author":"WM Lees","year":"2019","unstructured":"Lees, W.M., Wunderlich, A., Jeavons, P., Hale, P., Souryal, M.: Deep learning classification of 3.5-GHZ band spectrograms with applications to spectrum sensing. IEEE Trans. Cogn. Commun. Netw. 5, 224\u2013236 (2019)","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"issue":"11","key":"28_CR8","doi-asserted-by":"publisher","first-page":"10760","DOI":"10.1109\/TVT.2018.2868698","volume":"67","author":"F Meng","year":"2018","unstructured":"Meng, F., Chen, P., Wu, L., Wang, X.: Automatic modulation classification: a deep learning enabled approach. IEEE Trans. Veh. Technol. 67(11), 10760\u201310772 (2018). https:\/\/doi.org\/10.1109\/TVT.2018.2868698","journal-title":"IEEE Trans. Veh. Technol."},{"key":"28_CR9","unstructured":"O\u2019Shea, T., West, N.: Radio machine learning dataset generation with gnu radio. In: Proceedings of the GNU Radio Conference, vol. 1, no. 1 (2016). https:\/\/pubs.gnuradio.org\/index.php\/grcon\/article\/view\/11"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J.: Convolutional radio modulation recognition networks. CoRR (2016). http:\/\/arxiv.org\/abs\/1602.04105","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"O\u2019Mahony, G.D., Harris, P.J., Murphy, C.C.: Detecting interference in wireless sensor network received samples: a machine learning approach. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/WF-IoT48130.2020.9221332","DOI":"10.1109\/WF-IoT48130.2020.9221332"},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"Peng, S., Jiang, H., Wang, H., Alwageed, H., Yao, Y.D.: Modulation classification using convolutional neural network based deep learning model. In: 2017 26th Wireless and Optical Communication Conference (WOCC), pp. 1\u20135 (2017). https:\/\/doi.org\/10.1109\/WOCC.2017.7929000","DOI":"10.1109\/WOCC.2017.7929000"},{"issue":"9","key":"28_CR13","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/MCOM.2018.1800153","volume":"56","author":"S Riyaz","year":"2018","unstructured":"Riyaz, S., Sankhe, K., Ioannidis, S., Chowdhury, K.: Deep learning convolutional neural networks for radio identification. IEEE Commun. Mag. 56(9), 146\u2013152 (2018). https:\/\/doi.org\/10.1109\/MCOM.2018.1800153","journal-title":"IEEE Commun. Mag."},{"key":"28_CR14","unstructured":"Schmidt, M., Block, D., Meier, U.: CRAWDAD dataset owl\/interference (v. 2019\u201302-12) (2019). https:\/\/crawdad.org\/owl\/interference\/20190212"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Taylor, G., Middleton, C., Fernando, X.: A question of scarcity: spectrum and Canada\u2019s urban core. J. Inf. Pol. 7, 120\u2013163 (2017). http:\/\/www.jstor.org\/stable\/10.5325\/jinfopoli.7.2017.0120","DOI":"10.5325\/jinfopoli.7.1.0120"},{"key":"28_CR16","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MWC.2019.8641417","volume":"26","author":"L Wan","year":"2019","unstructured":"Wan, L., Guo, Z., Chen, X.: Enabling efficient 5G NR and 4G LTE coexistence. IEEE Wirel. Commun. 26, 6\u20138 (2019). https:\/\/doi.org\/10.1109\/MWC.2019.8641417","journal-title":"IEEE Wirel. Commun."},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Ziegler, J.L., Arn, R.T., Chambers, W.: Modulation recognition with GNU Radio, Keras, and HackRF. In: 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), pp. 1\u20133 (2017). https:\/\/doi.org\/10.1109\/DySPAN.2017.7920747","DOI":"10.1109\/DySPAN.2017.7920747"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97124-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T13:49:36Z","timestamp":1648648176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97124-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030971236","9783030971243"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97124-3_28","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/simutools.eai-conferences.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}