{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:04:46Z","timestamp":1761663886459,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T00:00:00Z","timestamp":1550793600000},"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":[[2019,2,22]]},"DOI":"10.1145\/3301293.3302374","type":"proceedings-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T22:12:13Z","timestamp":1550873533000},"page":"69-74","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Towards Self-Driving Radios"],"prefix":"10.1145","author":[{"given":"Samuel","family":"Joseph","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]},{"given":"Rakesh","family":"Misra","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]},{"given":"Sachin","family":"Katti","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,2,22]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"3GPP. NR: Physical channels and modulation . TS 38.211 Mar. 2018.  3GPP. NR: Physical channels and modulation . TS 38.211 Mar. 2018."},{"key":"e_1_3_2_1_2_1","unstructured":"3GPP. NR: Physical layer procedures for control (Release 15) . TS 38.213 Mar. 2018.  3GPP. NR: Physical layer procedures for control (Release 15) . TS 38.213 Mar. 2018."},{"volume-title":"AAAI Conference on Artificial Intelligence","year":"2018","author":"Chinchali S.","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/EW.2010.5483527"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7996332"},{"key":"e_1_3_2_1_6_1","unstructured":"ITU. IMT Vision: Framework and overall objectives of future development of IMT for 2020 and beyond . Technical report Sept. 2015.  ITU. IMT Vision: Framework and overall objectives of future development of IMT for 2020 and beyond . Technical report Sept. 2015."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"F. Mismar and B. Evans. Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells. arXiv July 2017.  F. Mismar and B. Evans. Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells. arXiv July 2017.","DOI":"10.1109\/ACSSC.2018.8645168"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057090"},{"volume-title":"BlackHat USA","year":"2015","author":"Wang Z.","key":"e_1_3_2_1_12_1"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2006.256990"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7997286"},{"key":"e_1_3_2_1_15_1","unstructured":"C. Zhang P. Patras and H. Haddadi. Deep learning in mobile and wireless networking: A survey. arXiv preprint arXiv:1803.04311 2018.  C. Zhang P. Patras and H. Haddadi. Deep learning in mobile and wireless networking: A survey. arXiv preprint arXiv:1803.04311 2018."}],"event":{"name":"HotMobile '19: The 20th International Workshop on Mobile Computing Systems and Applications","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"],"location":"Santa Cruz CA USA","acronym":"HotMobile '19"},"container-title":["Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3301293.3302374","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3301293.3302374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:58:02Z","timestamp":1750208282000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3301293.3302374"}},"subtitle":["Physical-Layer Control using Deep Reinforcement Learning"],"short-title":[],"issued":{"date-parts":[[2019,2,22]]},"references-count":15,"alternative-id":["10.1145\/3301293.3302374","10.1145\/3301293"],"URL":"https:\/\/doi.org\/10.1145\/3301293.3302374","relation":{},"subject":[],"published":{"date-parts":[[2019,2,22]]},"assertion":[{"value":"2019-02-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}