{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T13:01:04Z","timestamp":1725454864427},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","funder":[{"name":"PFI:BIC","award":["1632051"]},{"name":"CCF","award":["2007159"]},{"name":"CNS","award":["1909177,1617627,1814551,1950171,1949753,1617412"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,15]]},"DOI":"10.1145\/3485730.3485928","type":"proceedings-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T11:41:35Z","timestamp":1636630895000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":51,"title":["NELoRa"],"prefix":"10.1145","author":[{"given":"Chenning","family":"Li","sequence":"first","affiliation":[{"name":"Michigan State University"}]},{"given":"Hanqing","family":"Guo","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Shuai","family":"Tong","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Xiao","family":"Zeng","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Zhichao","family":"Cao","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Mi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Qiben","family":"Yan","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Li","family":"Xiao","sequence":"additional","affiliation":[{"name":"Michigan State University"}]},{"given":"Jiliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Yunhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]}],"member":"320","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Retrieved by Nov 19th","author":"Alliance LoRa","year":"2020","unstructured":"LoRa Alliance . Retrieved by Nov 19th 2020 . A technical overview of LoRa and LoRaWAN. In https:\/\/lora-alliance.org\/resource-hub\/what-lorawanr. LoRa Alliance. Retrieved by Nov 19th 2020. A technical overview of LoRa and LoRaWAN. In https:\/\/lora-alliance.org\/resource-hub\/what-lorawanr."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3388915"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOM.1973.1091721"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430714"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"M. Centenaro L. Vangelista A. Zanella and M. Zorzi. 2016. Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios. IEEE Wireless Communications (2016). M. Centenaro L. Vangelista A. Zanella and M. Zorzi. 2016. Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios. IEEE Wireless Communications (2016).","DOI":"10.1109\/MWC.2016.7721743"},{"key":"e_1_3_2_1_6_1","volume-title":"DeepSense: Enabling Carrier Sense in Low-Power Wide Area Networks Using Deep Learning. arXiv:1904.10607 [cs]","author":"Chan Justin","year":"2019","unstructured":"Justin Chan , Anran Wang , Arvind Krishnamurthy , and Shyamnath Gollakota . 2019. DeepSense: Enabling Carrier Sense in Low-Power Wide Area Networks Using Deep Learning. arXiv:1904.10607 [cs] ( 2019 ). Justin Chan, Anran Wang, Arvind Krishnamurthy, and Shyamnath Gollakota. 2019. DeepSense: Enabling Carrier Sense in Low-Power Wide Area Networks Using Deep Learning. arXiv:1904.10607 [cs] (2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"The Wigner distribution---A tool for time-frequency signal analysis---Part II: Discrete time signals. Philips Research","author":"Claasen TACM","year":"1980","unstructured":"TACM Claasen and Wolfgang Mecklenbr\u00e4uker . 1980. The Wigner distribution---A tool for time-frequency signal analysis---Part II: Discrete time signals. Philips Research ( 1980 ). TACM Claasen and Wolfgang Mecklenbr\u00e4uker. 1980. The Wigner distribution---A tool for time-frequency signal analysis---Part II: Discrete time signals. Philips Research (1980)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302506.3310396"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of ACM\/IEEE IPSN.","author":"Demetri S.","unstructured":"S. Demetri , M. Z\u00fa\u00f1iga , G. P. Picco , F. Kuipers , L. Bruzzone , and T. Telkamp . 2019. Automated Estimation of Link Quality for LoRa: A Remote Sensing Approach . In Proceedings of ACM\/IEEE IPSN. S. Demetri, M. Z\u00fa\u00f1iga, G. P. Picco, F. Kuipers, L. Bruzzone, and T. Telkamp. 2019. Automated Estimation of Link Quality for LoRa: A Remote Sensing Approach. In Proceedings of ACM\/IEEE IPSN."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2018.00013"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2017.8335670"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098845"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241559"},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of USENIX NSDI.","author":"Gadre Akshay","year":"2020","unstructured":"Akshay Gadre , Revathy Narayanan , Anh Luong , Anthony Rowe , Bob Iannucci , and Swarun Kumar . 2020 . Frequency Configuration for Low-Power Wide-Area Networks in a Heartbeat . In Proceedings of USENIX NSDI. Akshay Gadre, Revathy Narayanan, Anh Luong, Anthony Rowe, Bob Iannucci, and Swarun Kumar. 2020. Frequency Configuration for Low-Power Wide-Area Networks in a Heartbeat. In Proceedings of USENIX NSDI."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN48710.2020.00031"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2017.7926071"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430719"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of USENIX NSDI.","author":"Hessar Mehrdad","year":"2019","unstructured":"Mehrdad Hessar , Ali Najafi , and Shyamnath Gollakota . 2019 . NetScatter: Enabling Large-Scale Backscatter Networks . In Proceedings of USENIX NSDI. Mehrdad Hessar, Ali Najafi, and Shyamnath Gollakota. 2019. NetScatter: Enabling Large-Scale Backscatter Networks. In Proceedings of USENIX NSDI."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of USENIX NSDI.","author":"Hessar Mehrdad","year":"2020","unstructured":"Mehrdad Hessar , Ali Najafi , Vikram Iyer , and Shyamnath Gollakota . 2020 . TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds . In Proceedings of USENIX NSDI. Mehrdad Hessar, Ali Najafi, Vikram Iyer, and Shyamnath Gollakota. 2020. TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds. In Proceedings of USENIX NSDI."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP49622.2020.9259397"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of EWSN.","author":"Iova Oana","year":"2017","unstructured":"Oana Iova , Amy Murphy , Gian Pietro Picco , Lorenzo Ghiro , Davide Molteni , Federico Ossi , and Francesca Cagnacci . 2017 . LoRa from the city to the mountains: Exploration of hardware and environmental factors . In Proceedings of EWSN. Oana Iova, Amy Murphy, Gian Pietro Picco, Lorenzo Ghiro, Davide Molteni, Federico Ossi, and Francesca Cagnacci. 2017. LoRa from the city to the mountains: Exploration of hardware and environmental factors. In Proceedings of EWSN."},{"key":"e_1_3_2_1_23_1","volume-title":"Communication algorithms via deep learning. arXiv preprint arXiv:1805.09317","author":"Kim Hyeji","year":"2018","unstructured":"Hyeji Kim , Yihan Jiang , Ranvir Rana , Sreeram Kannan , Sewoong Oh , and Pramod Viswanath . 2018. Communication algorithms via deep learning. arXiv preprint arXiv:1805.09317 ( 2018 ). Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath. 2018. Communication algorithms via deep learning. arXiv preprint arXiv:1805.09317 (2018)."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of USENIX OSDI.","author":"Lai Fan","year":"2021","unstructured":"Fan Lai , Xiangfeng Zhu , Harsha V. Madhyastha , and Mosharaf Chowdhury . 2021 . Oort: Efficient Federated Learning via Guided Participant Selection . In Proceedings of USENIX OSDI. Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient Federated Learning via Guided Participant Selection. In Proceedings of USENIX OSDI."},{"key":"e_1_3_2_1_25_1","volume-title":"Deep AI Enabled Ubiquitous Wireless Sensing: A Survey. ACM Computing Surveys (CSUR)","author":"Li Chenning","year":"2020","unstructured":"Chenning Li , Zhichao Cao , and Yunhao Liu . 2020. Deep AI Enabled Ubiquitous Wireless Sensing: A Survey. ACM Computing Surveys (CSUR) ( 2020 ). Chenning Li, Zhichao Cao, and Yunhao Liu. 2020. Deep AI Enabled Ubiquitous Wireless Sensing: A Survey. ACM Computing Surveys (CSUR) (2020)."},{"key":"e_1_3_2_1_26_1","volume-title":"Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710","author":"Li Hao","year":"2016","unstructured":"Hao Li , Asim Kadav , Igor Durdanovic , Hanan Samet , and Hans Peter Graf . 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 ( 2016 ). Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293534"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN48710.2020.00-50"},{"key":"e_1_3_2_1_29_1","volume-title":"Pushing the limits of transmission concurrency for low power wireless networks. ACM Transactions on Sensor Networks","author":"Liu Daibo","year":"2020","unstructured":"Daibo Liu , Zhichao Cao , Mengshu Hou , Huigui Rong , and Hongbo Jiang . 2020. Pushing the limits of transmission concurrency for low power wireless networks. ACM Transactions on Sensor Networks ( 2020 ). Daibo Liu, Zhichao Cao, Mengshu Hou, Huigui Rong, and Hongbo Jiang. 2020. Pushing the limits of transmission concurrency for low power wireless networks. ACM Transactions on Sensor Networks (2020)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419193"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488784"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3439956"},{"key":"e_1_3_2_1_33_1","volume-title":"Visualizing data using t-SNE. Journal of machine learning research","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008. Visualizing data using t-SNE. Journal of machine learning research ( 2008 ). Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research (2008)."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR.","author":"McMahan H. Brendan","year":"2017","unstructured":"H. Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Ag\u00fcera y Arcas . 2017 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR. H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2788405"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"e_1_3_2_1_37_1","volume-title":"An introduction to machine learning communications systems. arXiv preprint arXiv:1702.00832","author":"O'Shea Timothy J","year":"2017","unstructured":"Timothy J O'Shea and Jakob Hoydis . 2017. An introduction to machine learning communications systems. arXiv preprint arXiv:1702.00832 ( 2017 ). Timothy J O'Shea and Jakob Hoydis. 2017. An introduction to machine learning communications systems. arXiv preprint arXiv:1702.00832 (2017)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230567"},{"key":"e_1_3_2_1_39_1","volume-title":"Smoothed pseudo Wigner-Ville distribution as an alternative to Fourier transform in rats. Autonomic Neuroscience","author":"de Souza Neto Edmundo Pereira","year":"2001","unstructured":"Edmundo Pereira de Souza Neto , Marc-Antoine Custaud , Jean Frutoso , Laurence Somody , Claude Gharib , and Jacques-Olivier Fortrat . 2001. Smoothed pseudo Wigner-Ville distribution as an alternative to Fourier transform in rats. Autonomic Neuroscience ( 2001 ). Edmundo Pereira de Souza Neto, Marc-Antoine Custaud, Jean Frutoso, Laurence Somody, Claude Gharib, and Jacques-Olivier Fortrat. 2001. Smoothed pseudo Wigner-Ville distribution as an alternative to Fourier transform in rats. Autonomic Neuroscience (2001)."},{"key":"e_1_3_2_1_40_1","volume-title":"Retrieved by Nov 19th","author":"Research ABI","year":"2020","unstructured":"ABI Research . Retrieved by Nov 19th 2020 . NB-IoT and LTE-M Issues to Boost LoRa and Sigfox Near and Long-term Lead in LPWA Network Connections. In https:\/\/tinyurl.com\/2026-cellular-iot. ABI Research. Retrieved by Nov 19th 2020. NB-IoT and LTE-M Issues to Boost LoRa and Sigfox Near and Long-term Lead in LPWA Network Connections. In https:\/\/tinyurl.com\/2026-cellular-iot."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2004.829161"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3452296.3472931"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/NORTHC.1995.485019"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3388913"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155509"},{"key":"e_1_3_2_1_46_1","volume-title":"Fundamentals of wireless communication","author":"Tse David","unstructured":"David Tse and Pramod Viswanath . 2005. Fundamentals of wireless communication . Cambridge university press . David Tse and Pramod Viswanath. 2005. Fundamentals of wireless communication. Cambridge university press."},{"key":"e_1_3_2_1_47_1","volume-title":"Deep learning for wireless physical layer: Opportunities and challenges. China Communications","author":"Wang Tianqi","year":"2017","unstructured":"Tianqi Wang , Chao-Kai Wen , Hanqing Wang , Feifei Gao , Tao Jiang , and Shi Jin . 2017. Deep learning for wireless physical layer: Opportunities and challenges. China Communications ( 2017 ). Tianqi Wang, Chao-Kai Wen, Hanqing Wang, Feifei Gao, Tao Jiang, and Shi Jin. 2017. Deep learning for wireless physical layer: Opportunities and challenges. China Communications (2017)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2019.8888038"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360024"},{"key":"e_1_3_2_1_50_1","volume-title":"Proceedings of EWSN.","author":"Yao Yuguang","year":"2019","unstructured":"Yuguang Yao , Zijun Ma , and Zhichao Cao . 2019 . LoSee: Long-Range Shared Bike Communication System Based on LoRaWAN Protocol .. In Proceedings of EWSN. Yuguang Yao, Zijun Ma, and Zhichao Cao. 2019. LoSee: Long-Range Shared Bike Communication System Based on LoRaWAN Protocol.. In Proceedings of EWSN."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419185"},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of USENIX OSDI.","author":"Yeo Hyunho","year":"2018","unstructured":"Hyunho Yeo , Youngmok Jung , Jaehong Kim , Jinwoo Shin , and Dongsu Han . 2018 . Neural Adaptive Content-aware Internet Video Delivery . In Proceedings of USENIX OSDI. Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han. 2018. Neural Adaptive Content-aware Internet Video Delivery. In Proceedings of USENIX OSDI."},{"key":"e_1_3_2_1_53_1","volume-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","author":"Zhang Kai","year":"2017","unstructured":"Kai Zhang , Wangmeng Zuo , Yunjin Chen , Deyu Meng , and Lei Zhang . 2017. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising . IEEE Transactions on Image Processing ( 2017 ). Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Transactions on Image Processing (2017)."},{"key":"e_1_3_2_1_54_1","volume-title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","author":"Zhang Kai","year":"2018","unstructured":"Kai Zhang , Wangmeng Zuo , and Lei Zhang . 2018. FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising . IEEE Transactions on Image Processing ( 2018 ). Kai Zhang, Wangmeng Zuo, and Lei Zhang. 2018. FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising. IEEE Transactions on Image Processing (2018)."},{"key":"e_1_3_2_1_55_1","volume-title":"Deep Learning in the Era of Edge Computing: Challenges and Opportunities","author":"Zhang Mi","year":"2020","unstructured":"Mi Zhang , Faen Zhang , Nicholas D Lane , Yuanchao Shu , Xiao Zeng , Biyi Fang , Shen Yan , and Hui Xu. 2020. Deep Learning in the Era of Edge Computing: Challenges and Opportunities . Fog Computing : Theory and Practice ( 2020 ). Mi Zhang, Faen Zhang, Nicholas D Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu. 2020. Deep Learning in the Era of Edge Computing: Challenges and Opportunities. Fog Computing: Theory and Practice (2020)."},{"key":"e_1_3_2_1_56_1","volume-title":"Proceedings on ICML.","author":"Zhao Mingmin","year":"2017","unstructured":"Mingmin Zhao , Shichao Yue , Dina Katabi , Tommi S Jaakkola , and Matt T Bianchi . 2017 . Learning Sleep Stages from Radio Signals - A Conditional Adversarial Architecture . In Proceedings on ICML. Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S Jaakkola, and Matt T Bianchi. 2017. Learning Sleep Stages from Radio Signals - A Conditional Adversarial Architecture. In Proceedings on ICML."}],"event":{"name":"SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems","location":"Coimbra Portugal","acronym":"SenSys '21","sponsor":["SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGCOMM ACM Special Interest Group on Data Communication","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems","SIGBED ACM Special Interest Group on Embedded Systems","SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485730.3485928","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T15:42:52Z","timestamp":1672933372000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485730.3485928"}},"subtitle":["Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation"],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":56,"alternative-id":["10.1145\/3485730.3485928","10.1145\/3485730"],"URL":"https:\/\/doi.org\/10.1145\/3485730.3485928","relation":{},"subject":[],"published":{"date-parts":[[2021,11,15]]},"assertion":[{"value":"2021-11-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}