{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:06:11Z","timestamp":1780099571222,"version":"3.54.0"},"reference-count":120,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"(APC)"},{"DOI":"10.13039\/501100012511","name":"Escuela Polit\u00e9cnica Nacional","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012511","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3599475","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T18:22:02Z","timestamp":1755282122000},"page":"144699-144732","source":"Crossref","is-referenced-by-count":3,"title":["A Systematic Literature Mapping of Machine Learning for Handover Optimization in Cellular Networks"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4965-055X","authenticated-orcid":false,"given":"Viviana","family":"Parraga-Villamar","sequence":"first","affiliation":[{"name":"Departamento de Electr&#x00F3;nica Telecomunicaciones y Redes de Informaci&#x00F3;n, Escuela Polit&#x00E9;cnica Nacional, Quito, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2794-3079","authenticated-orcid":false,"given":"Danny S.","family":"Guam\u00e1n","sequence":"additional","affiliation":[{"name":"Departamento de Electr&#x00F3;nica Telecomunicaciones y Redes de Informaci&#x00F3;n, Escuela Polit&#x00E9;cnica Nacional, Quito, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6162-3429","authenticated-orcid":false,"given":"Felipe","family":"Grijalva","sequence":"additional","affiliation":[{"name":"Universidad San Francisco de Quito (USFQ), Quito, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-4980","authenticated-orcid":false,"given":"Pablo","family":"Lupera-Morillo","sequence":"additional","affiliation":[{"name":"Departamento de Electr&#x00F3;nica Telecomunicaciones y Redes de Informaci&#x00F3;n, Escuela Polit&#x00E9;cnica Nacional, Quito, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Proactive mobility management of UEs using sequence-to-sequence modeling","author":"Yajnanarayana","year":"2021","journal-title":"arXiv:2110.07262"},{"issue":"6","key":"ref2","doi-asserted-by":"crossref","first-page":"5824","DOI":"10.11591\/ijece.v10i6.pp5824-5831","article-title":"Q-learning vertical handover scheme in two-tier LTE\u2014A networks","volume":"10","author":"Bathich","year":"2020","journal-title":"Int. J. Electr. Comput. Eng. (IJECE)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC54779.2022.9977541"},{"issue":"1","key":"ref4","first-page":"362","article-title":"Survey of vertical handover decision algorithms","volume":"2","author":"Ambudkar","year":"2013","journal-title":"Int. J. Innov. Eng. Technol."},{"key":"ref5","article-title":"Handover and load balancing self-optimization models in 5G mobile networks","volume":"42","author":"Saad","year":"2023","journal-title":"Eng. Sci. Technol., Int. J."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"118907","DOI":"10.1109\/ACCESS.2019.2937405","article-title":"A survey on handover management: From LTE to NR","volume":"7","author":"Tayyab","year":"2019","journal-title":"IEEE Access"},{"issue":"4","key":"ref7","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. Surveys Tuts."},{"key":"ref8","article-title":"SOMNet: Self-optimizing mobility management for resilient 5G heterogeneous networks","volume":"52","author":"Alhammadi","year":"2024","journal-title":"Eng. Sci. Technol., Int. J."},{"issue":"4","key":"ref9","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.3390\/app10041354","article-title":"Velocity-aware handover self-optimization management for next generation networks","volume":"10","author":"Alhammadi","year":"2020","journal-title":"Appl. Sci."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01589-1"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1706\/1\/012161"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"129","DOI":"10.12720\/jcm.18.2.129-134","article-title":"Minimization of handover decisions with quality of service using fuzzy logic prediction model","volume":"2023","author":"Balkhi","year":"2023","journal-title":"J. Commun."},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108736"},{"key":"ref14","first-page":"1","article-title":"Handover prediction integrated with service migration in 5G systems","volume-title":"Proc. ICC - IEEE Int. Conf. Commun. (ICC)","author":"Abdah"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3067503"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3215684"},{"key":"ref17","first-page":"1","article-title":"Handover measurement in mobile cellular networks: Analysis and applications to LTE","volume-title":"Proc. IEEE Int. Conf. Commun. (ICC)","author":"Nguyen"},{"key":"ref18","first-page":"989","article-title":"A review on the evolution of cellular technologies","volume-title":"Proc. 16th Int. Bhurban Conf. Appl. Sci. Technol. (IBCAST)","author":"Ud Din Arshad"},{"key":"ref19","first-page":"794","article-title":"Machine learning based handover management for improved QoE in LTE","volume-title":"Proc. IEEE\/IFIP Netw. Oper. Manage. Symp. (NOMS)","author":"Ali"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-016-1348-2"},{"key":"ref21","first-page":"1","article-title":"Minimizing handover performance degradation due to LTE self organized mobility load balancing","volume-title":"Proc. IEEE 77th Veh. Technol. Conf. (VTC Spring)","author":"Mwanje"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.4218\/etrij.2022-0459"},{"key":"ref23","first-page":"860","article-title":"Distributed reduced-state SARSA algorithm for dynamic channel allocation in cellular networks featuring traffic mobility","volume-title":"Proc. IEEE Int. Conf. Commun. (ICC)","author":"Lilith"},{"key":"ref24","first-page":"349","article-title":"5G handover using reinforcement learning","volume-title":"Proc. IEEE 3rd 5G World Forum (5GWF)","author":"Yajnanarayana"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118226","article-title":"An intelligent energy efficient handover mechanism with adaptive discontinuous reception in next generation telecommunication networks","volume":"209","author":"Goyal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref26","first-page":"1","article-title":"Reducing latency: Improving handover procedure using machine learning","volume-title":"Proc. IEEE 93rd Veh. Technol. Conf. (VTC-Spring)","author":"Zhohov"},{"key":"ref27","first-page":"41","article-title":"A ML based empirical model for next cell ID prediction","volume-title":"Mobilkommunikation: Technologien Und Anwendungen","author":"Sunil","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9839024"},{"issue":"1","key":"ref29","first-page":"2","article-title":"Brief survey: Machine learning in handover cellular network","volume":"47","author":"P\u00e1rraga-Villamar","year":"2023","journal-title":"Eng. Proc"},{"key":"ref30","article-title":"Hierarchical deep Q-learning based handover in wireless networks with dual connectivity","author":"Rivera","year":"2023","journal-title":"arXiv:2301.05391"},{"key":"ref31","volume-title":"Machine Learning","author":"Mitchell","year":"1997"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3297300"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2021.0120298"},{"issue":"3","key":"ref35","first-page":"613","article-title":"Handoff using machine learning techniques","volume":"7","author":"Krishna","year":"2022","journal-title":"Int. J. Innov. Sci. Res. Technol."},{"key":"ref36","first-page":"1","article-title":"Mobile user trajectory prediction based on machine learning","volume-title":"Proc. IEEE 95th Veh. Technol. Conf. (VTC-Spring)","author":"Liu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2023-Spring57618.2023.10200542"},{"issue":"3","key":"ref38","doi-asserted-by":"crossref","DOI":"10.1016\/j.eij.2023.100389","article-title":"Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G","volume":"24","author":"Priyanka","year":"2023","journal-title":"Egyptian Informat. J."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/594\/1\/012027"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/MTTW51045.2020.9245065"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"183505","DOI":"10.1109\/ACCESS.2020.3027258","article-title":"Mobility management in emerging ultra-dense cellular networks: A survey, outlook, and future research directions","volume":"8","author":"Zaidi","year":"2020","journal-title":"IEEE Access"},{"issue":"16","key":"ref42","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.3390\/electronics13163223","article-title":"A comprehensive survey on machine learning methods for handover optimization in 5G networks","volume":"13","author":"Thillaigovindhan","year":"2024","journal-title":"Electronics"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/8845070"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-022-01076-3"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-014-1506-1"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2015.03.007"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/2043106.2043114"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3551660.3560913"},{"key":"ref49","first-page":"1","article-title":"Partially blind handovers for mmWave new radio aided by sub-6 GHz LTE signaling","volume-title":"Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops)","author":"Mismar"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/MASS52906.2021.00011"},{"key":"ref51","first-page":"953","article-title":"Joint implementation of several LTE-SON functions","volume-title":"Proc. IEEE Globecom Workshops (GC Wkshps)","author":"Dinh"},{"key":"ref52","first-page":"524","article-title":"Novel algorithm to reduce handover failure rate in 5G networks","volume-title":"Proc. IEEE 3rd 5G World Forum (5GWF)","author":"Mishra"},{"issue":"3","key":"ref53","doi-asserted-by":"crossref","first-page":"746","DOI":"10.3390\/s22030746","article-title":"Enhancing handover for 5G mmWave mobile networks using jump Markov linear system and deep reinforcement learning","volume":"22","author":"Chiputa","year":"2022","journal-title":"Sensors"},{"issue":"4","key":"ref54","first-page":"2068","article-title":"Using AI in wireless communication system for resource management and optimisation","volume":"8","author":"Mardan","year":"2020","journal-title":"Periodicals Eng. Natural Sci."},{"key":"ref55","first-page":"1","article-title":"A kernel methods approach to reducing handover occurrences within LTE","volume-title":"Proc. 18th Eur. Wireless Conf. Eur. Wireless","author":"Sinclair"},{"key":"ref56","first-page":"1","article-title":"Deep learning based localization and HO optimization in 5G NR networks","volume-title":"Proc. Int. Conf. Localization GNSS (ICL-GNSS)","author":"Klus"},{"key":"ref57","first-page":"1","article-title":"Distributed cooperative Q-learning for mobility-sensitive handover optimization in LTE SON","volume-title":"Proc. IEEE Symp. Comput. Commun. (ISCC)","author":"Mwanje"},{"key":"ref58","first-page":"188","article-title":"Optimization of handover problem using Q-learning for LTE network","volume-title":"Proc. 30th Int. Conf. Microelectron. (ICM)","author":"Adel"},{"issue":"3","key":"ref59","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1109\/TNSM.2021.3073244","article-title":"A service-centric Q-learning algorithm for mobility robustness optimization in LTE","volume":"18","author":"Mari-Altozano","year":"2021","journal-title":"IEEE Trans. Netw. Service Manage."},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12120"},{"key":"ref61","first-page":"111","article-title":"Unsupervised learning for detection of mobility related anomalies in commercial LTE networks","volume-title":"Proc. Eur. Conf. Netw. Commun. (EuCNC)","author":"Moysen"},{"key":"ref62","first-page":"818","article-title":"AQ-learning approach for mobility robustness optimization in lte-son","volume-title":"Proc. 15th IEEE Int. Conf. Commun. Technol.","author":"Qin"},{"key":"ref63","first-page":"194","article-title":"LTE handover parameters optimization using Q-learning technique","volume-title":"Proc. IEEE 61st Int. Midwest Symp. Circuits Syst. (MWSCAS)","author":"Abdelmohsen"},{"key":"ref64","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.107056","article-title":"Mobility-aware load balancing for reliable self-organization networks: Multi-agent deep reinforcement learning","volume":"202","author":"Mohajer","year":"2020","journal-title":"Rel. Eng. Syst. Saf."},{"key":"ref65","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.comnet.2014.10.027","article-title":"Load balancing and handover joint optimization in LTE networks using fuzzy logic and reinforcement learning","volume":"76","author":"Mu\u00f1oz","year":"2015","journal-title":"Comput. Netw."},{"key":"ref66","first-page":"76","article-title":"Fuzzy Q-learning for mobility robustness optimization in wireless networks","volume-title":"Proc. IEEE Globecom Workshops (GC Wkshps)","author":"Klein"},{"key":"ref67","first-page":"36","article-title":"Predicting a user\u2019s next cell with supervised learning based on channel states","volume-title":"Proc. IEEE 14th Workshop Signal Process. Adv. Wireless Commun. (SPAWC)","author":"Chen"},{"key":"ref68","first-page":"2035","article-title":"Cognitive network function for mobility robustness optimization in cellular networks","volume-title":"Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)","author":"Parameswaran"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01718-w"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.14419\/ijet.v7i2.31.13401"},{"issue":"1","key":"ref71","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TNSM.2016.2522080","article-title":"Cognitive cellular networks: A Q-learning framework for self-organizing networks","volume":"13","author":"Mwanje","year":"2016","journal-title":"IEEE Trans. Netw. Service Manage."},{"key":"ref72","first-page":"594","article-title":"Handover scheme with enode-B pre-selection and parameter self-optimization for LTE\u2014A heterogeneous networks","volume-title":"Proc. Int. Conf. Mach. Learn. Cybern. (ICMLC)","volume":"2","author":"Ferng"},{"key":"ref73","first-page":"1","article-title":"Parameter optimization for LTE handover using an advanced SOM algorithm","volume-title":"Proc. IEEE 77th Veh. Technol. Conf. (VTC Spring)","author":"Sinclair"},{"issue":"6","key":"ref74","doi-asserted-by":"crossref","first-page":"5260","DOI":"10.1109\/TVT.2017.2711582","article-title":"Optimization of handover parameters for LTE\/LTE\u2014A in-building systems","volume":"67","author":"Castro-Hernandez","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref75","first-page":"1","article-title":"Cellular network traffic prediction incorporating handover: A graph convolutional approach","volume-title":"Proc. 17th Annu. IEEE Int. Conf. Sens., Commun., Netw. (SECON)","author":"Zhao"},{"issue":"5","key":"ref76","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1109\/TVT.2013.2251922","article-title":"An advanced SOM algorithm applied to handover management within LTE","volume":"62","author":"Sinclair","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref77","doi-asserted-by":"crossref","first-page":"72281","DOI":"10.1109\/ACCESS.2023.3294990","article-title":"A new method to improve frequent-handover problem in high-mobility communications using RIC and machine learning","volume":"11","author":"Prananto","year":"2023","journal-title":"IEEE Access"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13174-0_2"},{"key":"ref79","first-page":"1359","article-title":"Q-learning-based prediction of channel quality after handover in mobile networks","volume-title":"Proc. IEEE 25th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC)","author":"Becvar"},{"key":"ref80","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.comcom.2017.06.009","article-title":"Self organizing method for handover performance optimization in LTE-advanced network","volume":"110","author":"Chaudhuri","year":"2017","journal-title":"Comput. Commun."},{"key":"ref81","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.comcom.2018.10.011","article-title":"Handover optimization scheme for LTE-advance networks based on AHP-TOPSIS and Q-learning","volume":"133","author":"Goyal","year":"2019","journal-title":"Comput. Commun."},{"key":"ref82","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.aej.2023.04.013","article-title":"A comparative study of machine learning-based load balancing in high-speed train system","volume":"72","author":"Gures","year":"2023","journal-title":"Alexandria Eng. J."},{"key":"ref83","first-page":"1","article-title":"NXG04\u20132: A negotiation-based network selection scheme for next-generation mobile systems","volume-title":"Proc. IEEE Globecom","author":"Song"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/2775278","article-title":"Using an efficient technique based on dynamic learning period for improving delay in AI-based handover","volume":"2021","author":"Majid","year":"2021","journal-title":"Mobile Inf. Syst."},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v22.i2.pp1124-1134"},{"key":"ref86","first-page":"1","article-title":"Novel technique in 4G handover parameter tuning and prediction using statistical trend analysis and supervised machine learning","volume-title":"Proc. Int. Symp. Netw., Comput. Commun. (ISNCC)","author":"Elmahdy"},{"key":"ref87","first-page":"1","article-title":"Signal overhead reduction for AI-assisted conditional handover preparation","volume-title":"Proc. 25th Mobile Commun.-Technol. Appl. ITG-Symp.","author":"Gharouni"},{"key":"ref88","first-page":"466","article-title":"Double deep reinforcement learning assisted handovers in 5G and beyond cellular networks","volume-title":"Proc. 15th Int. Conf. Commun. Syst. Netw. (COMSNETS)","author":"Verma"},{"key":"ref89","first-page":"25","article-title":"An improved handover decision algorithm for 5G heterogeneous networks","volume-title":"Proc. IEEE 15th Malaysia Int. Conf. Commun. (MICC)","author":"Khan"},{"key":"ref90","doi-asserted-by":"crossref","first-page":"166932","DOI":"10.1109\/ACCESS.2021.3136129","article-title":"An SDN\/ML-based adaptive cell selection approach for HetNets: A real-world case study in london, U.K.","volume":"9","author":"Alablani","year":"2021","journal-title":"IEEE Access"},{"key":"ref91","doi-asserted-by":"crossref","DOI":"10.1016\/j.phycom.2020.101133","article-title":"Intelligent handover decision scheme using double deep reinforcement learning","volume":"42","author":"Mollel","year":"2020","journal-title":"Phys. Commun."},{"key":"ref92","first-page":"1","article-title":"Base station prediction and proactive mobility management in virtual cells using recurrent neural networks","volume-title":"Proc. IEEE 18th Wireless Microw. Technol. Conf. (WAMICON)","author":"Wickramasuriya"},{"issue":"4","key":"ref93","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.3390\/s23042191","article-title":"A reinforcement learning handover parameter adaptation method based on LSTM-aided digital twin for UDN","volume":"23","author":"He","year":"2023","journal-title":"Sensors"},{"key":"ref94","first-page":"348","article-title":"Intelligent handover management in 5G mobile networks based on recurrent neural networks","volume-title":"Proc. 3rd Int. Conf. Adv. Inf. Commun. Technol. (AICT)","author":"Shubyn"},{"key":"ref95","first-page":"1","article-title":"Relay selection for 5G new radio via artificial neural networks","volume-title":"Proc. 22nd Int. Symp. Wireless Pers. Multimedia Commun. (WPMC)","author":"Aldossari"},{"key":"ref96","first-page":"869","article-title":"Deep learning based adaptive handover optimization for ultra-dense 5G mobile networks","volume-title":"Proc. IEEE 15th Int. Conf. Adv. Trends Radioelectronics, Telecommun. Comput. Eng. (TCSET)","author":"Shubyn"},{"key":"ref97","first-page":"648","article-title":"Machine-learning-based predictive handover","volume-title":"Proc. IFIP\/IEEE Int. Symp. Integr. Netw. Manage. (IM)","author":"Masri"},{"key":"ref98","first-page":"124","article-title":"Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells","volume-title":"Proc. SPIE","volume":"11574","author":"Liu"},{"key":"ref99","first-page":"244","article-title":"Deep-mobility: A deep learning approach for an efficient and reliable 5G handover","volume-title":"Proc. Int. Conf. Wireless Commun. Signal Process. Netw. (WiSPNET)","author":"Paropkari"},{"issue":"6","key":"ref100","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/TMC.2017.2762668","article-title":"The SMART handoff policy for millimeter wave heterogeneous cellular networks","volume":"17","author":"Sun","year":"2018","journal-title":"IEEE Trans. Mobile Comput."},{"issue":"1","key":"ref101","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MVT.2019.2959065","article-title":"Prediction-based conditional handover for 5G mm-wave networks: A deep-learning approach","volume":"15","author":"Lee","year":"2020","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref102","first-page":"1","article-title":"Dynamic fuzzy Q-learning for handover parameters optimization in 5G multi-tier networks","volume-title":"Proc. Int. Conf. Wireless Commun. Signal Process. (WCSP)","author":"Wu"},{"key":"ref103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/8245306","article-title":"State aware-based prioritized experience replay for handover decision in 5G ultradense networks","volume":"2022","author":"Wu","year":"2022","journal-title":"Wireless Commun. Mobile Comput."},{"issue":"10","key":"ref104","doi-asserted-by":"crossref","first-page":"5995","DOI":"10.1109\/TMC.2022.3188212","article-title":"Mobility management in 5G and beyond: A novel smart handover with adaptive time-to-trigger and hysteresis margin","volume":"22","author":"Karmakar","year":"2023","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref105","doi-asserted-by":"crossref","first-page":"97942","DOI":"10.1109\/ACCESS.2021.3095555","article-title":"Autonomous mobility management for 5G ultra-dense HetNets via reinforcement learning with tile coding function approximation","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref106","doi-asserted-by":"crossref","first-page":"117910","DOI":"10.1109\/ACCESS.2021.3107325","article-title":"Fuzzy logic and accelerated reinforcement learning-based user association for dense C-RANs","volume":"9","author":"Rodoshi","year":"2021","journal-title":"IEEE Access"},{"key":"ref107","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.comcom.2021.04.020","article-title":"Multi-criteria handover mobility management in 5G cellular network","volume":"174","author":"Palas","year":"2021","journal-title":"Comput. Commun."},{"key":"ref108","first-page":"1","article-title":"Efficient handover algorithm in 5G networks using deep learning","volume-title":"Proc. IEEE Global Commun. Conf. (GLOBECOM)","author":"Huang"},{"key":"ref109","first-page":"1","article-title":"Intelligent mobile handover prediction for zero downtime edge application mobility","volume-title":"Proc. IEEE Global Commun. Conf. (GLOBECOM)","author":"Uniyal"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06673-5"},{"issue":"6","key":"ref111","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/JSAC.2023.3273705","article-title":"Enabling proportionally-fair mobility management with reinforcement learning in 5G networks","volume":"41","author":"Prado","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref112","doi-asserted-by":"crossref","first-page":"77830","DOI":"10.1109\/ACCESS.2021.3083554","article-title":"Machine learning-based mobility robustness optimization under dynamic cellular networks","volume":"9","author":"Nguyen","year":"2021","journal-title":"IEEE Access"},{"key":"ref113","first-page":"1","article-title":"Handover optimization via asynchronous multi-user deep reinforcement learning","volume-title":"Proc. IEEE Int. Conf. Commun. (ICC)","author":"Wang"},{"key":"ref114","first-page":"74","article-title":"Optimized performance evaluation of a Q-learning hard handover algorithm for load balancing","volume-title":"Proc. IEEE Microw. Theory Techn. Wireless Commun. (MTTW)","author":"Carlos"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/icc42927.2021.9500877"},{"issue":"2","key":"ref116","doi-asserted-by":"crossref","first-page":"199","DOI":"10.3390\/telecom2020013","article-title":"A hybrid user mobility prediction approach for handover management in mobile networks","volume":"2","author":"Bahra","year":"2021","journal-title":"Telecom"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26684"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685781"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/ICCITECHNOL.2011.5762703"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-82322-1_9"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11126082.pdf?arnumber=11126082","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:02:09Z","timestamp":1755910929000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11126082\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":120,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3599475","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}