{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T13:24:29Z","timestamp":1782739469309,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology, Republic of China","award":["MOST 110-2221-E-126-001"],"award-info":[{"award-number":["MOST 110-2221-E-126-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.<\/jats:p>","DOI":"10.3390\/s22062328","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5603-5200","authenticated-orcid":false,"given":"JainShing","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-394X","authenticated-orcid":false,"given":"Chun-Hung Richard","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-3645","authenticated-orcid":false,"given":"Yu-Chen","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8233-6071","authenticated-orcid":false,"given":"Praveen Kumar","family":"Donta","sequence":"additional","affiliation":[{"name":"Research Unit of Distributed Systems, TU Wien, 1040 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5896","DOI":"10.1109\/ACCESS.2016.2597169","article-title":"Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hewa, T., Braeken, A., Ylianttila, M., and Liyanage, M. (2020, January 7\u201311). Multi-Access Edge Computing and Blockchain-based Secure Telehealth System Connected with 5G and IoT. Proceedings of the GLOBECOM 2020\u20142020 IEEE Global Communications Conference, Taipei, Taiwan.","DOI":"10.1109\/GLOBECOM42002.2020.9348125"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, F., Wang, A., Zhang, Y., Ni, Z., and Hua, J. (2021). Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT. Sensors, 21.","DOI":"10.3390\/s21144798"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1109\/JIOT.2018.2825334","article-title":"Simultaneous Wireless Information and Power Transfer for Internet of Things Sensor Networks","volume":"5","author":"Chae","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Masood, Z.A., and Choi, Y. (2021). Energy-efficient optimal power allocation for swipt based iot-enabled smart meter. Sensors, 21.","DOI":"10.3390\/s21237857"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/ACCESS.2016.2645704","article-title":"Delay and energy trade-off in energy harvesting multi-hop wireless networks with inter-session network coding and successive interference cancellation","volume":"5","author":"Liu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tran, T.-N., and Voznak, M. (2021). Switchable Coupled Relays Aid Massive Non-Orthogonal Multiple Access Networks with Transmit Antenna Selection and Energy Harvesting. Sensors, 21.","DOI":"10.3390\/s21041101"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/JSTSP.2007.914876","article-title":"Dynamic Spectrum Management: Complexity and Duality","volume":"2","author":"Luo","year":"2008","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MCOM.2014.6736746","article-title":"Five disruptive technology directions for 5G","volume":"52","author":"Boccardi","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Y., Luo, J., Xu, W., Vucic, N., Pateromichelakis, E., and Caire, G. (2017, January 19\u201322). A Joint Scheduling and Resource Allocation Scheme for Millimeter Wave Heterogeneous Networks. Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925545"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, Z., Xu, W., Xu, H., Shi, J., and Chen, M. (2016, January 4\u20138). User Association, Resource Allocation and Power Control in Load-Coupled Heterogeneous Networks. Proceedings of the 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA.","DOI":"10.1109\/GLOCOMW.2016.7849073"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1049\/iet-com.2015.0153","article-title":"Dynamic femtocell resource allocation for managing inter-tier interference in downlink of heterogeneous networks","volume":"10","author":"Saeed","year":"2016","journal-title":"IET Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6942","DOI":"10.1109\/TVT.2017.2661698","article-title":"Three-Stage Resource Allocation Algorithm for Energy-Efficient Heterogeneous Networks","volume":"66","author":"Coskun","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14591","DOI":"10.1109\/ACCESS.2018.2810216","article-title":"Energy-Efficient Resource Allocation for Heterogeneous Wireless Network With Multi-Homed User Equipments","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7043","DOI":"10.1109\/TCOMM.2019.2936813","article-title":"Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks","volume":"67","author":"Le","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Y., and Zhang, W. (2016, January 3\u20136). Energy efficient resource allocation for heterogeneous cloud radio access networks with user cooperation and QoS guarantees. Proceedings of the 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar.","DOI":"10.1109\/WCNC.2016.7565103"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zou, S., Liu, N., Pan, Z., and You, X. (2016, January 15\u201318). Joint Power and Resource Allocation for Non-Uniform Topologies in Heterogeneous Networks. Proceedings of the 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, China.","DOI":"10.1109\/VTCSpring.2016.7504311"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/TWC.2017.2786255","article-title":"Incomplete CSI Based Resource Optimization in SWIPT Enabled Heterogeneous Networks: A Non-Cooperative Game Theoretic Approach","volume":"17","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/TMC.2012.178","article-title":"Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks","volume":"12","author":"Chen","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/TVT.2013.2280617","article-title":"Pricing-Based Multiresource Allocation in OFDMA Cognitive Radio Networks: An Energy Efficiency Perspective","volume":"63","author":"Xu","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7540","DOI":"10.1109\/TVT.2017.2673245","article-title":"Energy-Efficient Noncooperative Power Control in Small-Cell Networks","volume":"66","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","first-page":"1","article-title":"Radio Resource Allocation for Device-to-Device Underlay Communication Using Hypergraph Theory","volume":"15","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_23","unstructured":"Zhang, R., Cheng, X., Yang, L., and Jiao, B. (2013, January 7\u201310). Interference-aware graph based resource sharing for device-to-device communications underlaying cellular networks. Proceedings of the 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1109\/TCOMM.2013.071013.120787","article-title":"Device-to-device communications underlaying cellular networks","volume":"61","author":"Feng","year":"2013","journal-title":"IEEE Trans. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6119","DOI":"10.1109\/TVT.2015.2472995","article-title":"Energy-Efficient Joint Resource Allocation and Power Control for D2D Communications","volume":"65","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6255","DOI":"10.1109\/TWC.2020.3001736","article-title":"Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches","volume":"19","author":"Meng","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100480","DOI":"10.1109\/ACCESS.2019.2930115","article-title":"Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach","volume":"7","author":"Nguyen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kang, C., Ma, T., Teng, Y., and Guo, D. (2018, January 27\u201330). Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learning. Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA.","DOI":"10.1109\/VTCFall.2018.8690757"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/LWC.2014.2315039","article-title":"Massive MIMO With Joint Power Control","volume":"3","author":"Choi","year":"2014","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kang, C., Teng, Y., Li, S., Zheng, W., and Fang, J. (2019, January 22\u201325). Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks. Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA.","DOI":"10.1109\/VTCFall.2019.8891448"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8577","DOI":"10.1109\/JIOT.2019.2921159","article-title":"Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications","volume":"6","author":"Qiu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_33","unstructured":"3GPP (2015). Evolved Universal Terrestrial Radio Access (E-UTRA): Physical Layer Procedures (3GPP), 3GPP. ts 36.213, dec. 2015."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TWC.2019.2900890","article-title":"Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks","volume":"18","author":"Kim","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1109\/ACCESS.2017.2657221","article-title":"An Interference Coordination-Based Distributed Resource Allocation Scheme in Heterogeneous Cellular Networks","volume":"5","author":"Song","year":"2017","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Trakas, P., Adelantado, F., Zorba, N., and Verikoukis, C. (2017, January 4\u20138). A QoE-aware joint resource allocation and dynamic pricing algorithm for heterogeneous networks. Proceedings of the GLOBECOM 2017\u20142017 IEEE Global Communications Conference, Singapore.","DOI":"10.1109\/GLOCOM.2017.8254131"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1109\/TVT.2014.2374237","article-title":"Learning Based Frequency- and Time-Domain Inter-Cell Interference Coordination in HetNets","volume":"64","author":"Simsek","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ghadimi, E., Calabrese, F.D., Peters, G., and Soldati, P. (2017, January 21\u201325). A reinforcement learning approach to power control and rate adaptation in cellular networks. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7997440"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/MCOM.2018.1701031","article-title":"Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges","volume":"56","author":"Calabrese","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sharma, S., Darak, S.J., and Srivastava, A. (2017, January 4\u20138). Energy saving in heterogeneous cellular network via transfer reinforcement learning based policy. Proceedings of the 2017 9th International Conference on Communication Systems and Networks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS.2017.7945411"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1109\/TWC.2017.2769644","article-title":"User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach","volume":"17","author":"Wei","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/TVT.2011.2176356","article-title":"A Game-Theoretic Framework for Interference Coordination in OFDMA Relay Networks","volume":"61","author":"Liang","year":"2011","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/TWC.2021.3107866","article-title":"Worst-Case Energy Efficiency in Secure SWIPT Networks with Rate-Splitting ID and Power-Splitting EH Receivers","volume":"21","author":"Lu","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10799","DOI":"10.1109\/JIOT.2019.2941897","article-title":"Robust Resource Allocation and Power Splitting in SWIPT Enabled Heterogeneous Networks: A Robust Minimax Approach","volume":"6","author":"Xu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/JSAC.2021.3118397","article-title":"Joint Coordinated Beamforming and Power Splitting Ratio Optimization in MU-MISO SWIPT-Enabled HetNets: A Multi-Agent DDQN-Based Approach","volume":"40","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_46","first-page":"4387","article-title":"Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks","volume":"7","author":"Omidkar","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Canese, L., Cardarilli, G., Di Nunzio, L., Fazzolari, R., Giardino, D., Re, M., and Span\u00f2, S. (2021). Multi-Agent Reinforcement Learning: A Review of Challenges and Applications. Appl. Sci., 11.","DOI":"10.1038\/s41598-021-94691-7"},{"key":"ref_48","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press. [2nd ed.]."},{"key":"ref_49","unstructured":"Goodfellow, J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201314). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, QC, Canada."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/COMST.2017.2783901","article-title":"Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges","volume":"20","author":"Perera","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1109\/TCOMM.2019.2961332","article-title":"Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination","volume":"68","author":"Mismar","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/JSTSP.2014.2334278","article-title":"Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems","volume":"8","author":"Alkhateeb","year":"2014","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/JSTSP.2016.2523924","article-title":"An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems","volume":"10","author":"Heath","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Schniter, P., and Sayeed, A. (2014, January 2\u20135). Channel estimation and precoder design for millimeter-wave communications: The sparse way. Proceedings of the 2014 48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2014.7094443"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1109\/TAP.2012.2235056","article-title":"Broadband Millimeter-Wave Propagation Measurements and Models Using Adaptive-Beam Antennas for Outdoor Urban Cellular Communications","volume":"61","author":"Rappaport","year":"2013","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_56","unstructured":"Rappaport, T.S., Heath, R.W., Daniels, R.C., and Murdock, J.N. (2014). Millimeter Wave Wireless Communications, Pearson."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1109\/COMST.2015.2499783","article-title":"Wireless charging technologies: Fundamentals, standards, and network applications","volume":"18","author":"Lu","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.1109\/TVT.2015.2436334","article-title":"Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer","volume":"65","author":"Ng","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"9834","DOI":"10.1109\/TVT.2016.2525821","article-title":"Energy-Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds With Hybrid Receivers","volume":"65","author":"Chang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sen, S., Santhapuri, N., Choudhury, R.R., and Nelakuditi, S. (2010, January 20\u201321). Successive interference cancellation: A back-of-the-envelope perspective. Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks, New York, NY, USA.","DOI":"10.1145\/1868447.1868464"},{"key":"ref_61","unstructured":"Bertsekas, D.P. (1995). Dynamic Programming and Optimal Control, Athena Scientific."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"25463","DOI":"10.1109\/ACCESS.2018.2831240","article-title":"Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_63","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_64","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Introduction to Reinforcement Learning, MIT Press. [1st ed.]."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"37328","DOI":"10.1109\/ACCESS.2018.2850226","article-title":"Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems","volume":"6","author":"Alkhateeb","year":"2018","journal-title":"IEEE Access"},{"key":"ref_66","unstructured":"Fudenberg, D., and Tirole, J. (1991). Game Theory, MIT Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:38:15Z","timestamp":1760135895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,17]]},"references-count":66,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062328"],"URL":"https:\/\/doi.org\/10.3390\/s22062328","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,17]]}}}