{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:36:58Z","timestamp":1781714218007,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"DOI":"10.1145\/3447993.3483275","type":"proceedings-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T09:00:31Z","timestamp":1635238831000},"page":"628-641","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["FIRE"],"prefix":"10.1145","author":[{"given":"Zikun","family":"Liu","sequence":"first","affiliation":[{"name":"University of Illinois Urbana-Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gagandeep","family":"Singh","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign and VMware Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenren","family":"Xu","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Vasisht","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Reconfigurable eco-system for next-generation end-to-end wireless. https:\/\/renew.rice.edu\/index.html","author":"Renew","year":"2019","unstructured":"Renew : Reconfigurable eco-system for next-generation end-to-end wireless. https:\/\/renew.rice.edu\/index.html , 2019 . Renew: Reconfigurable eco-system for next-generation end-to-end wireless. https:\/\/renew.rice.edu\/index.html, 2019."},{"key":"e_1_3_2_1_2_1","volume-title":"3rd Generation Partnership Project (3GPP)","author":"Evolved Universal Terrestrial Radio GPP.","year":"2021","unstructured":"3 GPP. Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification. Technical specification (ts) , 3rd Generation Partnership Project (3GPP) , 2021 . 3GPP. Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification. Technical specification (ts), 3rd Generation Partnership Project (3GPP), 2021."},{"key":"e_1_3_2_1_3_1","volume-title":"ver. 15.3. 0","author":"Technical Generation Partnership","year":"2018","unstructured":"3rd Generation Partnership Project (3GPP). 5g; nr; physical channels and modulation. Technical Specification 38.211 , ver. 15.3. 0 , 2018 . 3rd Generation Partnership Project (3GPP). 5g; nr; physical channels and modulation. Technical Specification 38.211, ver. 15.3. 0, 2018."},{"key":"e_1_3_2_1_4_1","volume-title":"Physical layer procedures for data (3gpp ts 38.214 version 16.2.0 release 16)","author":"Generation Partnership","year":"2020","unstructured":"3rd Generation Partnership Project (3GPP). Physical layer procedures for data (3gpp ts 38.214 version 16.2.0 release 16) . 2020 . 3rd Generation Partnership Project (3GPP). Physical layer procedures for data (3gpp ts 38.214 version 16.2.0 release 16). 2020."},{"key":"e_1_3_2_1_5_1","first-page":"317","volume-title":"11th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 14)","author":"Adib F.","year":"2014","unstructured":"F. Adib , Z. Kabelac , D. Katabi , and R. C. Miller . 3d tracking via body radio reflections . In 11th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 14) , pages 317 -- 329 , 2014 . F. Adib, Z. Kabelac, D. Katabi, and R. C. Miller. 3d tracking via body radio reflections. In 11th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 14), pages 317--329, 2014."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9048929"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2320099"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3380894"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3345438"},{"key":"e_1_3_2_1_10_1","first-page":"1","volume-title":"2017 International Conference on Engineering Technology and Technopreneurship (ICE2T)","author":"Zubir N. Z.","year":"2017","unstructured":"N. Z. binti Zubir , A. F. Ramli , and H. Basarudin . Optimization of wireless sensor networks mac protocols using machine learning; a survey . In 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T) , pages 1 -- 5 . IEEE, 2017 . N. Z. binti Zubir, A. F. Ramli, and H. Basarudin. Optimization of wireless sensor networks mac protocols using machine learning; a survey. In 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), pages 1--5. IEEE, 2017."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2016.7402270"},{"key":"e_1_3_2_1_12_1","first-page":"1","article-title":"Open, Programmable, and Virtualized 5G Networks","volume":"182","author":"Bonati L.","year":"2020","unstructured":"L. Bonati , M. Polese , S. D'Oro , S. Basagni , and T. Melodia . Open, Programmable, and Virtualized 5G Networks : State-of-the-Art and the Road Ahead. Computer Networks , 182 : 1 -- 28 , December 2020 . L. Bonati, M. Polese, S. D'Oro, S. Basagni, and T. Melodia. Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead. Computer Networks, 182:1--28, December 2020.","journal-title":"State-of-the-Art and the Road Ahead. Computer Networks"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/INISTA.2015.7276725"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411276.3412204"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2002.808163"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2009.2029693"},{"key":"e_1_3_2_1_17_1","first-page":"7","volume-title":"study on new radio access technology. In 3GPP TSG RAN Meeting","author":"Docomo N.","year":"2016","unstructured":"N. Docomo . New sid proposal : study on new radio access technology. In 3GPP TSG RAN Meeting , volume 71 , pages 7 -- 10 , 2016 . N. Docomo. New sid proposal: study on new radio access technology. In 3GPP TSG RAN Meeting, volume 71, pages 7--10, 2016."},{"key":"e_1_3_2_1_18_1","volume-title":"Ericsson and qualcomm pioneer new carrier aggregation capabilities for 5g. https:\/\/www.ericsson.com\/en\/news\/2020\/8\/5g-carrier-aggregation","author":"Ericsson Inc.","year":"2021","unstructured":"Ericsson Inc. Ericsson and qualcomm pioneer new carrier aggregation capabilities for 5g. https:\/\/www.ericsson.com\/en\/news\/2020\/8\/5g-carrier-aggregation , 2021 . Ericsson Inc. Ericsson and qualcomm pioneer new carrier aggregation capabilities for 5g. https:\/\/www.ericsson.com\/en\/news\/2020\/8\/5g-carrier-aggregation, 2021."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2015.7248526"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/VETECF.2011.6093291"},{"key":"e_1_3_2_1_21_1","volume-title":"T-mobile exec touts massive mimo for both tdd and fdd bands. https:\/\/www.fiercewireless.com\/tech\/t-mobile-exec-says-massive-mimo-can-be-used-tdd-and-fdd-bands","author":"Hardesty L.","year":"2020","unstructured":"L. Hardesty . T-mobile exec touts massive mimo for both tdd and fdd bands. https:\/\/www.fiercewireless.com\/tech\/t-mobile-exec-says-massive-mimo-can-be-used-tdd-and-fdd-bands , 2020 . L. Hardesty. T-mobile exec touts massive mimo for both tdd and fdd bands. https:\/\/www.fiercewireless.com\/tech\/t-mobile-exec-says-massive-mimo-can-be-used-tdd-and-fdd-bands, 2020."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2805902"},{"key":"e_1_3_2_1_23_1","volume-title":"beta-vae: Learning basic visual concepts with a constrained variational framework","author":"Higgins I.","year":"2016","unstructured":"I. Higgins , L. Matthey , A. Pal , C. Burgess , X. Glorot , M. Botvinick , S. Mohamed , and A. Lerchner . beta-vae: Learning basic visual concepts with a constrained variational framework . 2016 . I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework. 2016."},{"key":"e_1_3_2_1_24_1","first-page":"3","volume-title":"Proc. Neural Information Processing Systems (NeurIPS)","author":"Hinton G. E.","year":"1993","unstructured":"G. E. Hinton and R. S. Zemel . Autoencoders, minimum description length and helmholtz free energy . In Proc. Neural Information Processing Systems (NeurIPS) , pages 3 -- 10 , 1993 . G. E. Hinton and R. S. Zemel. Autoencoders, minimum description length and helmholtz free energy. In Proc. Neural Information Processing Systems (NeurIPS), pages 3--10, 1993."},{"key":"e_1_3_2_1_25_1","first-page":"1587","volume-title":"Proc. International Conference on Machine Learning (ICML)","volume":"70","author":"Hu Z.","year":"2017","unstructured":"Z. Hu , Z. Yang , X. Liang , R. Salakhutdinov , and E. P. Xing . Toward controlled generation of text . In Proc. International Conference on Machine Learning (ICML) , volume 70 , pages 1587 -- 1596 . PMLR, 2017 . Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov, and E. P. Xing. Toward controlled generation of text. In Proc. International Conference on Machine Learning (ICML), volume 70, pages 1587--1596. PMLR, 2017."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761962"},{"key":"e_1_3_2_1_27_1","first-page":"066","volume-title":"Proc. COST","volume":"273","author":"Hugl K.","unstructured":"K. Hugl , K. Kalliola , J. Laurila , Spatial reciprocity of uplink and downlink radio channels in fdd systems . In Proc. COST , volume 273 , page 066 . Citeseer, 2002. K. Hugl, K. Kalliola, J. Laurila, et al. Spatial reciprocity of uplink and downlink radio channels in fdd systems. In Proc. COST, volume 273, page 066. Citeseer, 2002."},{"key":"e_1_3_2_1_28_1","volume-title":"USENIX NSDI","author":"Jog S.","year":"2021","unstructured":"S. Jog , Z. Liu , A. Franques , V. Fernando , S. Abadal , J. Torrellas , and H. Hassanieh . One protocol to rule them all: Wireless network-on-chip using deep reinforcement learning . USENIX NSDI , 2021 . S. Jog, Z. Liu, A. Franques, V. Fernando, S. Abadal, J. Torrellas, and H. Hassanieh. One protocol to rule them all: Wireless network-on-chip using deep reinforcement learning. USENIX NSDI, 2021."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2008.ECP.738"},{"key":"e_1_3_2_1_30_1","volume-title":"Fdd massive mimo via ul\/dl channel covariance extrapolation and active channel sparsification. arXiv ePrint","author":"Khalilsarai M. B.","year":"2018","unstructured":"M. B. Khalilsarai , S. Haghighatshoar , X. Yi , and G. Caire . Fdd massive mimo via ul\/dl channel covariance extrapolation and active channel sparsification. arXiv ePrint , 2018 . M. B. Khalilsarai, S. Haghighatshoar, X. Yi, and G. Caire. Fdd massive mimo via ul\/dl channel covariance extrapolation and active channel sparsification. arXiv ePrint, 2018."},{"key":"e_1_3_2_1_31_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma D. P.","year":"2013","unstructured":"D. P. Kingma and M. Welling . Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 , 2013 . D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013."},{"key":"e_1_3_2_1_32_1","volume-title":"Variational graph auto-encoders. CoRR, abs\/1611.07308","author":"Kipf T. N.","year":"2016","unstructured":"T. N. Kipf and M. Welling . Variational graph auto-encoders. CoRR, abs\/1611.07308 , 2016 . T. N. Kipf and M. Welling. Variational graph auto-encoders. CoRR, abs\/1611.07308, 2016."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2018.8503086"},{"key":"e_1_3_2_1_34_1","first-page":"2539","volume-title":"Proc. Neural Information Processing Systems (NeurIPS)","author":"Kulkarni T. D.","year":"2015","unstructured":"T. D. Kulkarni , W. F. Whitney , P. Kohli , and J. B. Tenenbaum . Deep convolutional inverse graphics network . In Proc. Neural Information Processing Systems (NeurIPS) , pages 2539 -- 2547 , 2015 . T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. B. Tenenbaum. Deep convolutional inverse graphics network. In Proc. Neural Information Processing Systems (NeurIPS), pages 2539--2547, 2015."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19163445"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2014.30"},{"key":"e_1_3_2_1_37_1","first-page":"3","volume-title":"Proc. icml","volume":"30","author":"Maas A. L.","unstructured":"A. L. Maas , A. Y. Hannun , and A. Y. Ng . Rectifier nonlinearities improve neural network acoustic models . In Proc. icml , volume 30 , page 3 . Citeseer, 2013. A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3. Citeseer, 2013."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2014.7063445"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2010.092810.091092"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781316799895"},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Machine Learning (ICML)","author":"Mathieu E.","year":"2019","unstructured":"E. Mathieu , T. Rainforth , N. Siddharth , and Y. W. Teh . Disentangling disentanglement in variational autoencoders . In International Conference on Machine Learning (ICML) , 2019 . E. Mathieu, T. Rainforth, N. Siddharth, and Y. W. Teh. Disentangling disentanglement in variational autoencoders. In International Conference on Machine Learning (ICML), 2019."},{"key":"e_1_3_2_1_42_1","volume-title":"Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426","author":"McInnes L.","year":"2018","unstructured":"L. McInnes , J. Healy , and J. Melville . Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 , 2018 . L. McInnes, J. Healy, and J. Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1186\/1687-6180-2011-29"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2007.179"},{"key":"e_1_3_2_1_45_1","volume-title":"Deep learning based mimo communications. arXiv preprint arXiv:1707.07980","author":"O'Shea T. J.","year":"2017","unstructured":"T. J. O'Shea , T. Erpek , and T. C. Clancy . Deep learning based mimo communications. arXiv preprint arXiv:1707.07980 , 2017 . T. J. O'Shea, T. Erpek, and T. C. Clancy. Deep learning based mimo communications. arXiv preprint arXiv:1707.07980, 2017."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2014.6952172"},{"key":"e_1_3_2_1_47_1","volume-title":"Automatic differentiation in pytorch","author":"Paszke A.","year":"2017","unstructured":"A. Paszke , S. Gross , S. Chintala , G. Chanan , E. Yang , Z. DeVito , Z. Lin , A. Desmaison , L. Antiga , and A. Lerer . Automatic differentiation in pytorch . 2017 . A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in pytorch. 2017."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/PhDEDITS51180.2020.9315299"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2019.2931671"},{"key":"e_1_3_2_1_50_1","volume-title":"https:\/\/www.qualcomm.com\/documents\/fddtdd-comparison","author":"Qualcomm Inc. Fdd\/tdd comparison - key messages.","year":"2013","unstructured":"Qualcomm Inc. Fdd\/tdd comparison - key messages. https:\/\/www.qualcomm.com\/documents\/fddtdd-comparison , 2013 . Qualcomm Inc. Fdd\/tdd comparison - key messages. https:\/\/www.qualcomm.com\/documents\/fddtdd-comparison, 2013."},{"key":"e_1_3_2_1_51_1","volume-title":"Global Update on Spectrum for 4G & 5G. https:\/\/www.qualcomm.com\/media\/documents\/files\/spectrum-for-4g-and-5g.pdf","author":"Qualcomm Inc.","year":"2020","unstructured":"Qualcomm Inc. Global Update on Spectrum for 4G & 5G. https:\/\/www.qualcomm.com\/media\/documents\/files\/spectrum-for-4g-and-5g.pdf , 2020 . Qualcomm Inc. Global Update on Spectrum for 4G & 5G. https:\/\/www.qualcomm.com\/media\/documents\/files\/spectrum-for-4g-and-5g.pdf, 2020."},{"key":"e_1_3_2_1_52_1","unstructured":"Qualcomm Inc. Why carrier aggregation is needed for 5g and the latest qualcomm technologies breakthroughs making it possible. https:\/\/www.qualcomm.com\/news\/onq\/2021\/05\/21\/why-carrier-aggregation-needed-5g-and-latest-qualcomm-technologies-breakthroughs 2021.  Qualcomm Inc. Why carrier aggregation is needed for 5g and the latest qualcomm technologies breakthroughs making it possible. https:\/\/www.qualcomm.com\/news\/onq\/2021\/05\/21\/why-carrier-aggregation-needed-5g-and-latest-qualcomm-technologies-breakthroughs 2021."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJVT.2019.2962631"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2016.7869082"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348543.2348553"},{"key":"e_1_3_2_1_56_1","volume-title":"End-to-end learning of neuromorphic wireless systems for low-power edge artificial intelligence. arXiv preprint arXiv:2009.01527","author":"Skatchkovsky N.","year":"2020","unstructured":"N. Skatchkovsky , H. Jang , and O. Simeone . End-to-end learning of neuromorphic wireless systems for low-power edge artificial intelligence. arXiv preprint arXiv:2009.01527 , 2020 . N. Skatchkovsky, H. Jang, and O. Simeone. End-to-end learning of neuromorphic wireless systems for low-power edge artificial intelligence. arXiv preprint arXiv:2009.01527, 2020."},{"key":"e_1_3_2_1_57_1","volume-title":"Iris-030 sdr platform. https:\/\/skylarkwireless.com\/product\/iris-platform\/","author":"Wireless Skylark","year":"2021","unstructured":"Skylark Wireless . Iris-030 sdr platform. https:\/\/skylarkwireless.com\/product\/iris-platform\/ , 2021 . Skylark Wireless. Iris-030 sdr platform. https:\/\/skylarkwireless.com\/product\/iris-platform\/, 2021."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE-ASIA.2018.8552138"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934895"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929091"},{"key":"e_1_3_2_1_61_1","volume-title":"5g nr frequency bands --- Wikipedia, the free encyclopedia","author":"Wikipedia","year":"2021","unstructured":"Wikipedia contributors. 5g nr frequency bands --- Wikipedia, the free encyclopedia , 2021 . Wikipedia contributors. 5g nr frequency bands --- Wikipedia, the free encyclopedia, 2021."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2917131"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2929404"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2934851"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3019077"},{"key":"e_1_3_2_1_66_1","article-title":"Directional training for fdd massive mimo","author":"Zhang X.","year":"2018","unstructured":"X. Zhang , L. Zhong , and A. Sabharwal . Directional training for fdd massive mimo . IEEE Transactions on Wireless Communications , 2018 . X. Zhang, L. Zhong, and A. Sabharwal. Directional training for fdd massive mimo. IEEE Transactions on Wireless Communications, 2018.","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00768"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230579"}],"event":{"name":"ACM MobiCom '21: The 27th Annual International Conference on Mobile Computing and Networking","location":"New Orleans Louisiana","acronym":"ACM MobiCom '21","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 27th Annual International Conference on Mobile Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447993.3483275","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447993.3483275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:12Z","timestamp":1750193352000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447993.3483275"}},"subtitle":["enabling reciprocity for FDD MIMO systems"],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":68,"alternative-id":["10.1145\/3447993.3483275","10.1145\/3447993"],"URL":"https:\/\/doi.org\/10.1145\/3447993.3483275","relation":{},"subject":[],"published":{"date-parts":[[2021,10,25]]},"assertion":[{"value":"2021-10-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}