{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:13:06Z","timestamp":1792804386132,"version":"4.5.0"},"reference-count":85,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Program of Shanghai Municipal Education Commission of China","award":["2021-01-07-00-10-E00121"],"award-info":[{"award-number":["2021-01-07-00-10-E00121"]}],"id":[{"id":"https:\/\/ror.org\/05tewj457","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based medium access control (MAC) protocols. As the scale of the wireless ad hoc network (WANET) increases, traditional MAC solutions are becoming obsolete. Dynamic topology, resource allocation, interference management, limited bandwidth and energy constraint are crucial problems needing resolution for designing modern WANET architectures. In order for future MAC protocols to overcome the current limitations in frequently changing WANETs, more intelligence need to be deployed to maintain efficient communications. After introducing some classic RL schemes, we investigate the existing state-of-the-art MAC protocols and related solutions for WANETs according to the MAC reference model and discuss how each proposed protocol works and the challenging issues on the related MAC model components. Finally, this paper discusses future research directions on how RL can be used to enable MAC protocols for high performance.<\/jats:p>","DOI":"10.3390\/e25010101","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T01:42:44Z","timestamp":1672796564000},"page":"101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1848-4931","authenticated-orcid":false,"given":"Zhichao","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1954-7255","authenticated-orcid":false,"given":"Shengming","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2349-3891","authenticated-orcid":false,"given":"Ruoyu","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2659-3591","authenticated-orcid":false,"given":"Lige","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongchong","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kou, K., Lei, L., Zhang, L., Cai, S., and Shen, G. (2019, January 16\u201319). Intelligent Selection: A Neural Network-Based MAC Protocol-Selection Mechanism for Wireless Ad hoc Networks. Proceedings of the IEEE 19th International Conference on Communication Technology (ICCT), Xi\u00e1n, China.","DOI":"10.1109\/ICCT46805.2019.8947101"},{"key":"ref_2","unstructured":"Toh, C.K. (2012). Wireless ATM and Ad-Hoc Networks: Protocols and Architectures, Springer Science & Business Media."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1109\/LCOMM.2018.2844243","article-title":"A Deep Reinforcement Learning-Based Framework for Dynamic Resource Allocation in Multibeam Satellite Systems","volume":"22","author":"Hu","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tilwari, V., Bani-Bakr, A., Qamar, F., Hindia, M.N., Jayakody, D.N.K., and Hassan, R. (2021, January 12\u201313). Mobility and queue length aware routing approach for network stability and load balancing in MANET. Proceedings of the 2021 International Conference on Electrical Engineering and Informatics (ICEEI), Kuala Terengganu, Malaysia.","DOI":"10.1109\/ICEEI52609.2021.9611119"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Abbas, T., Qamar, F., Hindia, M.N., Hassan, R., Ahmed, I., and Aslam, M.I. (2020, January 27\u201329). Performance analysis of ad hoc on-demand distance vector routing protocol for MANET. Proceedings of the 2020 IEEE Student Conference on Research and Development (SCOReD), Johor, Malaysia.","DOI":"10.1109\/SCOReD50371.2020.9250989"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ahmadi, H., and Bouallegue, R. (2017, January 26\u201330). Exploiting machine learning strategies and RSSI for localization in wireless sensor networks: A survey. Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain.","DOI":"10.1109\/IWCMC.2017.7986447"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bangotra, D.K., Singh, Y., and Selwal, A. (2018, January 20\u201322). Machine Learning in Wireless Sensor Networks: Challenges and Opportunities. Proceedings of the Fifth International Conference on Parallel, Distributed and Grid Computing, Solan, India.","DOI":"10.1109\/PDGC.2018.8745845"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mastronarde, N., Modares, J., Wu, C., and Chakareski, J. (2016, January 4\u20138). Reinforcement Learning for Energy-Efficient Delay-Sensitive CSMA\/CA Scheduling. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA.","DOI":"10.1109\/GLOCOM.2016.7842209"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khamukhin, A.A., and Bertoldo, S. (2016, January 12\u201314). Spectral analysis of forest fire noise for early detection using wireless sensor networks. Proceedings of the International Siberian Conference on Control and Communications (SIBCON), Moscow, Russia.","DOI":"10.1109\/SIBCON.2016.7491654"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.comcom.2012.10.007","article-title":"A slotted CSMA based reinforcement learning approach for extending the lifetime of underwater acoustic wireless sensor networks","volume":"36","author":"Lu","year":"2013","journal-title":"Comput. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alhassan, I.B., and Mitchell, P.D. (2021). Packet flow based reinforcement learning MAC protocol for underwater acoustic sensor networks. Sensors, 21.","DOI":"10.3390\/s21072284"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gazi, F., Ahmed, N., Misra, S., and Wei, W. (2022). Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks. ACM Trans. Sens. Netw. (TOSN).","DOI":"10.1145\/3484201"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kassab, R., Destounis, A., Tsilimantos, D., and Debbah, M. (September, January 31). Multi-agent deep stochastic policy gradient for event based dynamic spectrum access. Proceedings of the 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK.","DOI":"10.1109\/PIMRC48278.2020.9217051"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/JIOT.2017.2759728","article-title":"A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of Things","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MCOM.2018.1701227","article-title":"On a novel deep-learning-based intelligent partially overlapping channel assignment in SDN-IoT","volume":"56","author":"Tang","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/JSAC.2019.2904366","article-title":"Reinforcement learning for real-time optimization in NB-IoT networks","volume":"37","author":"Jiang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3163","DOI":"10.1109\/TVT.2019.2897134","article-title":"Deep Reinforcement Learning Based Resource Allocation for V2V Communications","volume":"68","author":"Ye","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1109\/TWC.2019.2891629","article-title":"Liquid state machine learning for resource and cache management in LTE-U unmanned aerial vehicle (UAV) networks","volume":"18","author":"Chen","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.comcom.2019.12.054","article-title":"Intelligent resource allocation management for vehicles network: An A3C learning approach","volume":"151","author":"Chen","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIV.2019.2955905","article-title":"Combining planning and deep reinforcement learning in tactical decision making for autonomous driving","volume":"5","author":"Hoel","year":"2019","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Daknou, E., Tabbane, N., and Thaalbi, M. (2018, January 16\u201318). A MAC multi-channel scheme based on learning-automata for clustered VANETs. Proceedings of the 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, Poland.","DOI":"10.1109\/AINA.2018.00023"},{"key":"ref_22","unstructured":"Shah, A.S., Ilhan, H., and Tureli, U. (2019, January 10\u201312). Designing and Analysis of IEEE 802.11 MAC for UAVs Ad Hoc Networks. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MCOM.2017.1700323","article-title":"Adaptive Communication Protocols in Flying Ad Hoc Network","volume":"56","author":"Zheng","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rezwan, S., and Choi, W. (2021). A survey on applications of reinforcement learning in flying ad-hoc networks. Electronics, 10.","DOI":"10.3390\/electronics10040449"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20255","DOI":"10.1109\/TITS.2022.3180697","article-title":"A Novel MIMO-OFDM Based MAC Protocol for VANETs","volume":"23","author":"Karabulut","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102069","DOI":"10.1016\/j.adhoc.2019.102069","article-title":"A deep reinforcement learning for user association and power control in heterogeneous networks","volume":"102","author":"Ding","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5141","DOI":"10.1109\/TWC.2019.2933417","article-title":"Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks","volume":"18","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/JSAC.2019.2904329","article-title":"Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks","volume":"37","author":"Yu","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1109\/TWC.2019.2912754","article-title":"Deep reinforcement learning-based modulation and coding scheme selection in cognitive heterogeneous networks","volume":"18","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113428","DOI":"10.1109\/ACCESS.2021.3104509","article-title":"Survey and performance evaluation of multiple access schemes for next-generation wireless communication systems","volume":"9","author":"Shah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","unstructured":"Shah, A.S. (2022, January 26\u201329). A Survey From 1G to 5G Including the Advent of 6G: Architectures, Multiple Access Techniques, and Emerging Technologies. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Virtual."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/COMST.2021.3073009","article-title":"Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption","volume":"23","author":"Tang","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kakalou, I., Papadimitriou, G.I., Nicopolitidis, P., Sarigiannidis, P.G., and Obaidat, M.S. (2015, January 8\u201312). A Reinforcement learning-based cognitive MAC protocol. Proceedings of the IEEE International Conference on Communications (ICC), London, UK.","DOI":"10.1109\/ICC.2015.7249216"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/JSAC.2019.2933887","article-title":"Robust Coordinated Reinforcement Learning for MAC Design in Sensor Networks","volume":"37","author":"Nisioti","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deshpande, Y., Ayan, O., and Kellerer, W. (2022). Improving AoI via Learning-based Distributed MAC in Wireless Networks. arXiv.","DOI":"10.1109\/INFOCOMWKSHPS54753.2022.9798137"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5677","DOI":"10.1109\/JIOT.2020.2980586","article-title":"Deep-reinforcement-learning-based energy-efficient resource management for social and cognitive internet of things","volume":"7","author":"Yang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moon, S., Ahn, S., Son, K., Park, J., and Yi, Y. (2021, January 26\u201329). Neuro-DCF: Design of Wireless MAC via Multi-Agent Reinforcement Learning Approach. Proceedings of the Twenty-Second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Seoul, Republic of Korea.","DOI":"10.1145\/3466772.3467043"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chou, P.Y., Chen, W.Y., Wang, C.Y., Hwang, R.H., and Chen, W.T. (2020, January 7\u201311). Deep Reinforcement Learning for MEC Streaming with Joint User Association and Resource Management. Proceedings of the ICC 2020\u20142020 IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9149086"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108662","DOI":"10.1016\/j.comnet.2021.108662","article-title":"Distributed Reinforcement Learning for scalable wireless medium access in IoTs and sensor networks","volume":"202","author":"Dutta","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TCCN.2018.2809722","article-title":"Deep reinforcement learning for dynamic multichannel access in wireless networks","volume":"4","author":"Wang","year":"2018","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1109\/TNSE.2018.2835758","article-title":"QTCP: Adaptive congestion control with reinforcement learning","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/JSAC.2019.2933966","article-title":"Distributed learning for channel allocation over a shared spectrum","volume":"37","author":"Zafaruddin","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_43","unstructured":"Bao, S., and Fujii, T. (December, January 30). Q-learning Based p-pesistent CSMA MAC Protcol for Secondary User of Cognitive Radio Networks. Proceedings of the Third International Conference on Intelligent Networking and Collaborative Systems, Fukuoka, Japan."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10981","DOI":"10.1109\/TIE.2019.2960753","article-title":"SCSMA: A Smart CSMA\/CA Using Blind Learning for Wireless Sensor Networks","volume":"67","author":"Nguyen","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TCCN.2019.2957224","article-title":"Fast Q-learning for Improved Finite Length Performance of Irregular Repetition Slotted ALOHA","volume":"6","author":"Nisioti","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, H., Fang, H., Li, P., Yu, C., and Zhang, H. (2019, January 23\u201327). Enhancement Objective Q-learning MAC for Emergency Transmission. Proceedings of the 6th International Symposium on Electromagnetic Compatibility (ISEMC), Milan, Italy.","DOI":"10.1109\/ISEMC48616.2019.8986096"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/COMST.2017.2727878","article-title":"A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks","volume":"19","author":"Klaine","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1109\/TVT.2019.2961405","article-title":"Multi-agent deep reinforcement learning based spectrum allocation for D2D underlay communications","volume":"69","author":"Li","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"31823","DOI":"10.1109\/ACCESS.2020.2973140","article-title":"Deep reinforcement learning-based channel allocation for wireless lans with graph convolutional networks","volume":"8","author":"Nakashima","year":"2020","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, S., Chen, J., and Chen, J. (2020, January 19\u201321). A deep reinforcement learning based network management system in smart identifier network. Proceedings of the 2020 4th International Conference on Digital Signal Processing, Chengdu, China.","DOI":"10.1145\/3408127.3408173"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TSUSC.2019.2929935","article-title":"ST-DeLTA: A novel spatial-temporal value network aided deep learning based intelligent network traffic control system","volume":"5","author":"Tang","year":"2019","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/COMST.2018.2846401","article-title":"Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey","volume":"20","author":"Mao","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_53","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_54","unstructured":"Patterson, J., and Gibson, A. (2017). Deep Learning: A Practitioner\u2019s Approach, O\u2019Reilly Media, Inc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/COMST.2017.2768802","article-title":"State-of-the-Art Medium Access Control (MAC) Protocols for Underwater Acoustic Networks: A Survey Based on a MAC Reference Model","volume":"20","author":"Jiang","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep Learning in Mobile and Wireless Networking: A Survey","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zuriatunadhirah binti Zubir, N., Ramli, A.F., and Basarudin, H. (2017, January 18\u201320). Optimization of wireless sensor networks MAC protocols using machine learning; a survey. Proceedings of the 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICE2T.2017.8215998"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ifacol.2022.08.014","article-title":"Reinforcement Learning TDMA-Based MAC Scheduling in the Industrial Internet of Things: A Survey","volume":"55","author":"Kherbache","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kulin, M., Kazaz, T., De Poorter, E., and Moerman, I. (2021). A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer. Electronics, 10.","DOI":"10.3390\/electronics10030318"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1109\/COMST.2019.2924243","article-title":"Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues","volume":"21","author":"Sun","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCOM.001.2001146","article-title":"AI-Assisted MAC for Reconfigurable Intelligent-Surface-Aided Wireless Networks: Challenges and Opportunities","volume":"59","author":"Cao","year":"2021","journal-title":"IEEE Commun. Mag."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sharma, H., Haque, A., and Blaabjerg, F. (2021). Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey. Electronics, 10.","DOI":"10.3390\/electronics10091012"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bithas, P.S., Michailidis, E.T., Nomikos, N., Vouyioukas, D., and Kanatas, A.G. (2019). A Survey on Machine-Learning Techniques for UAV-Based Communications. Sensors, 19.","DOI":"10.3390\/s19235170"},{"key":"ref_64","unstructured":"Winston, P.H. (1992). Artificial Intelligence, Addison-Wesley Longman Publishing Co., Inc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MVT.2019.2903655","article-title":"Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges","volume":"14","author":"Xiong","year":"2019","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Technical Note: Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"101978","DOI":"10.1109\/ACCESS.2020.2996279","article-title":"A Learning-Automata-Based Congestion-Aware Scheme for Energy-Efficient Elastic Optical Networks","volume":"8","author":"Beletsioti","year":"2020","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TSMC.1974.5408453","article-title":"Learning Automata\u2014A Survey","volume":"SMC-4","author":"Narendra","year":"1974","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_69","unstructured":"Li, Y. (2017). Deep Reinforcement Learning: An Overview. arXiv."},{"key":"ref_70","first-page":"105","article-title":"A Study of Machine Learning in Wireless Sensor Network","volume":"4","author":"Khan","year":"2017","journal-title":"Int. J. Comput. Netw. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"De Rango, F., Cordeschi, N., and Ritacco, F. (2021, January 9\u201312). Applying Q-learning approach to CSMA Scheme to dynamically tune the contention probability. Proceedings of the 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49032.2021.9369509"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lee, D.J., Deng, Y., and Choi, Y.J. (2020, January 21\u201323). Back-off Improvement By Using Q-learning in IEEE 802.11p Vehicular Network. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC49870.2020.9289541"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, Y.W., and Kao, K.C. (2021, January 8\u201310). Study of Contention Window Adjustment for CSMA\/CA by Using Machine Learning. Proceedings of the 2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS), Tainan, Taiwan.","DOI":"10.23919\/APNOMS52696.2021.9562498"},{"key":"ref_74","first-page":"1","article-title":"Performance Enhancement of CSMA\/CA MAC Protocol Based on Reinforcement Learning","volume":"19","author":"Kim","year":"2021","journal-title":"J. Inf. Commun. Converg. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3500","DOI":"10.1109\/ACCESS.2018.2886216","article-title":"Deep reinforcement learning paradigm for performance optimization of channel observation\u2013based MAC protocols in dense WLANs","volume":"7","author":"Ali","year":"2018","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Lee, S., and Chung, S.H. (2022, January 5\u20138). Unslotted CSMA\/CA mechanism with reinforcement learning of Wi-SUN MAC layer. Proceedings of the 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain.","DOI":"10.1109\/ICUFN55119.2022.9829651"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Barbosa, P.F.C., Silva, B.A.d., Zanchettin, C., and de Moraes, R.M. (2020, January 25\u201328). Energy Consumption Optimization for CSMA\/CA Protocol Employing Machine Learning. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9128450"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"14078","DOI":"10.1109\/TVT.2020.3019687","article-title":"Deep Reinforcement Learning Aided Intelligent Access Control in Energy Harvesting Based WLAN","volume":"69","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s11276-016-1402-0","article-title":"A multi-state Q-learning based CSMA MAC protocol for wireless networks","volume":"24","author":"Kebriaei","year":"2018","journal-title":"Wirel. Netw."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Aboubakar, M., Roux, P., Kellil, M., and Bouabdallah, A. (2020, January 12\u201314). An Efficient and Adaptive Configuration of IEEE 802.15.4 MAC for Communication Delay Optimisation. Proceedings of the 2020 11th International Conference on Network of the Future (NoF), Bordeaux, France.","DOI":"10.1109\/NoF50125.2020.9249218"},{"key":"ref_81","first-page":"1048","article-title":"Rate adaptation with Q-learning in CSMA\/CA wireless networks","volume":"16","author":"Cho","year":"2020","journal-title":"J. Inf. Process. Syst."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Cho, S. (2021). Reinforcement Learning for Rate Adaptation in CSMA\/CA Wireless Networks. Advances in Computer Science and Ubiquitous Computing, Springer.","DOI":"10.1007\/978-981-15-9343-7_24"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, M., de Alfaro, L., and Garcia-Luna-Aceves, J. (2020, January 16\u201320). Using reinforcement learning in slotted aloha for ad-hoc networks. Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Alicante, Spain.","DOI":"10.1145\/3416010.3423231"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yin, H., Zhou, Z., Roy, S., and Sun, Y. (December, January 18). Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning. Proceedings of the 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Virtual.","DOI":"10.1109\/VTC2020-Fall49728.2020.9348485"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"45818","DOI":"10.1109\/ACCESS.2019.2909398","article-title":"Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments","volume":"7","author":"Mennes","year":"2019","journal-title":"IEEE Access"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/1\/101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:58:04Z","timestamp":1760119084000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/1\/101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,3]]},"references-count":85,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["e25010101"],"URL":"https:\/\/doi.org\/10.3390\/e25010101","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,3]]}}}