{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T09:33:24Z","timestamp":1761989604238,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030801250"},{"type":"electronic","value":"9783030801267"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-80126-7_46","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:11:23Z","timestamp":1625569883000},"page":"648-660","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Wireless Systems"],"prefix":"10.1007","author":[{"given":"Shu","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"46_CR1","unstructured":"3GPP TR 38.824, V16.0.0. Study on physical layer enhancements for NR ultra-reliable and low latency case, March 2019"},{"key":"46_CR2","unstructured":"3GPP TR 38.901, V16.1.0. Study on channel model for frequencies from 0.5 to 100 GHz, December 2019"},{"key":"46_CR3","unstructured":"3GPP TS 38.211, V16.3.0. NR; Physical channels and modulation, September 2020"},{"key":"46_CR4","unstructured":"3GPP TS 38.213, V16.3.0. NR; Physical layer procedures for control, September 2020"},{"key":"46_CR5","unstructured":"3GPP TS 38.214, V16.3.0. NR; Physical layer procedures for data, September 2020"},{"key":"46_CR6","unstructured":"3GPP TS 38.331, V16.1.0. NR; Radio resource control protocol specification, July 2020"},{"issue":"3","key":"46_CR7","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1109\/TETC.2018.2805718","volume":"8","author":"X He","year":"2020","unstructured":"He, X., Wang, K., Huang, H., Miyazaki, T., Wang, Y., Guo, S.: Green resource allocation based on deep reinforcement learning in content-centric IoT. IEEE Trans. Emerg. Top. Comput. 8(3), 781\u2013796 (2020)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Alkhateeb, A.: Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems. In: 2019 53rd Asilomar Conference on Signals, Systems, and Computers, pp. 800\u2013805 (2019)","DOI":"10.1109\/IEEECONF44664.2019.9048966"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Alkhateeb, A., Tepedelenlio\u011flu, C.: Generative adversarial estimation of channel covariance in vehicular millimeter wave systems. In: 2018 52nd Asilomar Conference on Signals, Systems, and Computers, pp. 1572\u20131576 (2018)","DOI":"10.1109\/ACSSC.2018.8645463"},{"issue":"4","key":"46_CR10","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","volume":"21","author":"NC Luong","year":"2019","unstructured":"Luong, N.C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutor. 21(4), 3133\u20133174 (2019)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"46_CR11","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"2014","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (2014)"},{"key":"46_CR12","unstructured":"Sun, S., Moon, S.: Practical scheduling algorithms with contiguous resource allocation for next-generation wireless systems. IEEE Wirel. Commun. Lett. 10(4), 725\u2013729 (2021)"},{"issue":"6","key":"46_CR13","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1109\/LWC.2020.2973152","volume":"9","author":"S Sun","year":"2020","unstructured":"Sun, S., Moon, S., Fwu, J.: Practical link adaptation algorithm with power density offsets for 5G uplink channels. IEEE Wirel. Commun. Lett. 9(6), 851\u2013855 (2020)","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"46_CR14","unstructured":"Tse, D.: Forward link multiuser diversity through proportional fair scheduling. Presentation at Bell Labs, August 1999"},{"key":"46_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comnet.2015.12.006","volume":"96","author":"EE Tsiropoulou","year":"2016","unstructured":"Tsiropoulou, E.E., Kapoukakis, A., Papavassiliou, S.: Uplink resource allocation in SC-FDMA wireless networks: a survey and taxonomy. Comput. Netw. 96, 1\u201328 (2016)","journal-title":"Comput. Netw."},{"key":"46_CR16","unstructured":"Wong, I.C., Oteri, O.F., McCoy, J.W.: Resource allocation in multi data stream communication link. U.S. Patent 7 911 934, March 2011"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wang, Y., Tang, J., Wang, J., Gursoy, M.C.: A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs. In: 2017 IEEE International Conference on Communications (ICC), pp. 1\u20136 (2017)","DOI":"10.1109\/ICC.2017.7997286"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Yan, H., Ashikhmin, A., Yang, H.: Optimally supporting IoT with cell-free massive MIMO. In: 2020 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136 (2020)","DOI":"10.1109\/GLOBECOM42002.2020.9348004"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Yan, H., Ashikhmin, A., Yang, H.: A scalable and energy efficient IoT system supported by cell-free massive MIMO. IEEE Internet Things J. (2021)","DOI":"10.1109\/JIOT.2021.3071781"},{"key":"46_CR20","doi-asserted-by":"publisher","first-page":"184045","DOI":"10.1109\/ACCESS.2020.3029518","volume":"8","author":"H Yan","year":"2020","unstructured":"Yan, H., Lu, I.T.: BS-UE association and power allocation in heterogeneous massive MIMO systems. IEEE Access 8, 184045\u2013184060 (2020)","journal-title":"IEEE Access"},{"issue":"4","key":"46_CR21","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/TVT.2019.2897134","volume":"68","author":"H Ye","year":"2019","unstructured":"Ye, H., Li, G.Y., Juang, B.F.: Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans. Veh. Technol. 68(4), 3163\u20133173 (2019)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"3","key":"46_CR22","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","volume":"21","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutor. 21(3), 2224\u20132287 (2019)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"11","key":"46_CR23","doi-asserted-by":"publisher","first-page":"5141","DOI":"10.1109\/TWC.2019.2933417","volume":"18","author":"N Zhao","year":"2019","unstructured":"Zhao, N., Liang, Y., Niyato, D., Pei, Y., Wu, M., Jiang, Y.: Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks. IEEE Trans. Wirel. Commun. 18(11), 5141\u20135152 (2019)","journal-title":"IEEE Trans. Wirel. Commun."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80126-7_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:33:04Z","timestamp":1625571184000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80126-7_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030801250","9783030801267"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80126-7_46","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}