{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:22:12Z","timestamp":1760149332495,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Mobile online gaming is constantly growing in popularity and is expected to be one of the most important applications of upcoming sixth generation networks. Nevertheless, it remains challenging for game providers to support it, mainly due to its intrinsic and ever-stricter need for service continuity in the presence of user mobility. In this regard, this paper proposes a machine learning strategy to forecast user channel conditions, aiming at guaranteeing a seamless service whenever a user is involved in a handover, i.e., moving from the coverage area of one base station towards another. In particular, the proposed channel condition prediction approach involves the exploitation of an echo state network, an efficient class of recurrent neural network, that is empowered with a genetic algorithm to perform parameter optimization. The echo state network is applied to improve user decisions regarding the selection of the serving base station, avoiding game breaks as much as possible to lower game lag time. The validity of the proposed framework is confirmed by simulations in comparison to the long short-term memory approach and another alternative method, aimed at thoroughly testing the accuracy of the learning module in forecasting user trajectories and in reducing game breaks or lag time, with a focus on a sixth generation network application scenario.<\/jats:p>","DOI":"10.3390\/jsan12040058","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:09:01Z","timestamp":1690333741000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Benedetta","family":"Picano","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7293-0210","authenticated-orcid":false,"given":"Leonardo","family":"Scommegna","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrico","family":"Vicario","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5934-3321","authenticated-orcid":false,"given":"Romano","family":"Fantacci","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1016\/j.comcom.2008.02.025","article-title":"Improving end-to-end quality-of-service in online multi-player wireless gaming networks","volume":"31","author":"Ghosh","year":"2008","journal-title":"Comput. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mangiante, S., Klas, G., Navon, A., GuanHua, Z., Ran, J., and Silva, M. (2017, January 25). VR is on the Edge: How to Deliver 360\u00b0 Videos in Mobile Networks. Proceedings of the Workshop on Virtual Reality and Augmented Reality Network, Virtual.","DOI":"10.1145\/3097895.3097901"},{"key":"ref_3","unstructured":"Huawei (2023, January 30). Cloud VR Solution White Paper. Available online: https:\/\/www.huawei.com\/en\/news\/2018\/9\/cloud-vr-."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17090","DOI":"10.1109\/JIOT.2021.3077497","article-title":"End-to-end delay bound for wireless uvr services over 6g terahertz communications","volume":"8","author":"Fantacci","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_5","unstructured":"Al-Eryani, Y.F., and Hossain, E. (2019). Delta-oma (d-oma): A new method for massive multiple access in 6g. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jalaparti, V., Caesar, M.C., Lee, S., Pang, J., and Merwe, J.V.D. (2012, January 22\u201323). Smog: A cloud platform for seamless wide area migration of online games. Proceedings of the 2012 11th Annual Workshop on Network and Systems Support for Games (NetGames), Venice, Italy.","DOI":"10.1109\/NetGames.2012.6404031"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bujari, A., Quadrio, G., Palazzi, C.E., Ronzani, D., Maggiorini, D., and Ripamonti, L.A. (2017, January 8\u201311). Network traffic analysis of the steam game system. Proceedings of the 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2017.7983221"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13638-022-02114-6","article-title":"A Survey on next Location Prediction Techniques, Applications, and Challenges","volume":"2022","author":"Chekol","year":"2022","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e3718","DOI":"10.1002\/ett.3718","article-title":"A matching game for tasks offloading in integrated edge-fog computing systems","volume":"31","author":"Chiti","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8533","DOI":"10.1109\/TVT.2019.2930363","article-title":"Nonlinear dynamic chaos theory framework for passenger demand forecasting in smart city","volume":"68","author":"Picano","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, Y., Wang, T., and Liew, S.C. (2018, January 20\u201324). Deep-reinforcement learning multiple access for heterogeneous wireless networks. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422168"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8440","DOI":"10.1109\/TVT.2018.2848294","article-title":"Deep learning for an effective nonorthogonal multiple access scheme","volume":"67","author":"Gui","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6007","DOI":"10.1109\/JIOT.2018.2867937","article-title":"Learning automata-based access class barring scheme for massive random access in machine-to-machine communications","volume":"6","author":"Di","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_14","unstructured":"Lukosevicius, M. (2012). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3039","DOI":"10.1109\/COMST.2019.2926625","article-title":"Artificial neural networks-based machine learning for wireless networks: A tutorial","volume":"21","author":"Chen","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Spyridonis, F., Daylamani-Zad, D., and O\u2019Brien, M.P. (2018, January 5\u20137). Efficient in-game communication in collaborative online multiplayer games. Proceedings of the 2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), W\u00fcrzburg, Germany.","DOI":"10.1109\/VS-Games.2018.8493420"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sapienza, A., Culotta, A., and Ferrara, E. (2019, January 20\u201323). Personality and behavior in role-based online games. Proceedings of the 2019 IEEE Conference on Games (CoG), London, UK.","DOI":"10.1109\/CIG.2019.8848027"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bertens, P., Guitart, A., and Peri\u00e1\u00f1ez, F. (2017, January 22\u201325). Games and big data: A scalable multi-dimensional churn prediction model. Proceedings of the 2017 IEEE Conference on Computational Intelligence and Games (CIG), New York, NY, USA.","DOI":"10.1109\/CIG.2017.8080412"},{"key":"ref_19","unstructured":"Cho, C.-S., Sohn, K.-M., Park, C.-J., and Kang, J.-H. (2010, January 7\u201310). Online game testing using scenario-based control of massive virtual users. Proceedings of the 2010 The 12th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, Republic of Korea."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, S., Wu, R., Tao, J., Qu, M., Li, H., and Fan, C. (2020, January 24\u201327). Multi-source data multi-task learning for profiling players in online games. Proceedings of the 2020 IEEE Conference on Games (CoG), Osaka, Japan.","DOI":"10.1109\/CoG47356.2020.9231585"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, W., Huang, T., Zeng, J., Yang, G., Cai, J., Chen, L., Mishra, S., and Liu, Y.E. (2019, January 20\u201323). Mining player in-game time spending regularity for churn prediction in free online games. Proceedings of the 2019 IEEE Conference on Games (CoG), London, UK.","DOI":"10.1109\/CIG.2019.8848033"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Voulgari, I., and Komis, V. (2011, January 4\u20136). On studying collaborative learning interactions in massively multiplayer online games. Proceedings of the 2011 Third International Conference on Games and Virtual Worlds for Serious Applications, Athens, Greece.","DOI":"10.1109\/VS-GAMES.2011.36"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012032","DOI":"10.1088\/1757-899X\/790\/1\/012032","article-title":"Trajectory Prediction Based on Machine Learning","volume":"790","author":"Su","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101441","DOI":"10.1109\/ACCESS.2019.2929430","article-title":"Exploring Trajectory Prediction Through Machine Learning Methods","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3080","DOI":"10.1109\/TNSE.2022.3140529","article-title":"Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things","volume":"9","author":"Liu","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abuzainab, N., Alrabeiah, M., Alkhateeb, A., and Sagduyu, Y. (2021, January 14\u201323). Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction. Proceedings of the 2021 IEEE International Conference On Communications Workshops (ICC Workshops), Montreal, QC, Canada.","DOI":"10.1109\/ICCWorkshops50388.2021.9473804"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Marasinghe, D., Rajatheva, N., and Latva-aho, M. (2021, January 7\u201311). LiDAR Aided Human Blockage Prediction for 6G. Proceedings of the 2021 IEEE Globecom Workshops (GC Wkshps), Madrid, Spain.","DOI":"10.1109\/GCWkshps52748.2021.9681949"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, P., Miao, L., and Jia, G. (2011, January 10\u201312). Clustered complex echo state networks for traffic forecasting with prior knowledge. Proceedings of the 2011 IEEE International Instrumentation and Measurement Technology Conference, Hangzhou, China.","DOI":"10.1109\/IMTC.2011.5944078"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, S., Wang, Z., and Ling, D. (2019, January 15\u201317). Echo state network prediction based on backtracking search optimization algorithm. Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China.","DOI":"10.1109\/ITNEC.2019.8729414"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chouikhi, N., Fdhila, R., Ammar, B., Rokbani, N., and Alimi, A.M. (2016, January 24\u201329). Single- and multi-objective particle swarm optimization of reservoir structure in echo state network. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727232"},{"key":"ref_31","unstructured":"Song, Y., Li, Y., Wang, Q., and Li, C. (2010, January 23\u201326). Multi-steps prediction of chaotic time series based on echo state network. Proceedings of the 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), Changsha, China."},{"key":"ref_32","unstructured":"Yu, L., Han, A., Wang, L., Jia, X., and Zhang, Z. (2016, January 26\u201328). Short-term load forecasting model for metro power supply system based on echo state neural network. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Adeleke, O.A. (2019, January 17\u201319). Echo-state networks for network traffic prediction. Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2019.8936255"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Petrov, V., Moltchanov, D., and Koucheryavy, Y. (2015, January 6\u20139). Interference and sinr in dense terahertz networks. Proceedings of the 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, USA.","DOI":"10.1109\/VTCFall.2015.7390991"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/JSAC.2019.2898756","article-title":"The impact of mobile blockers on millimeter wave cellular systems","volume":"37","author":"Jain","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TMTT.2016.2574851","article-title":"3-d millimeter-wave statistical channel model for 5g wireless system design","volume":"64","author":"Samimi","year":"2016","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1109\/TWC.2019.2913414","article-title":"Millimeter-wave base station diversity for 5g coordinated multipoint (comp) applications","volume":"18","author":"MacCartney","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Maccartney, G., Rappaport, T., and Rangan, S. (2017, January 4\u20138). Rapid fading due to human blockage in pedestrian crowds at 5g millimeter-wave frequencies. Proceedings of the GLOBECOM 2017\u20142017 IEEE Global Communications Conference, Singapore.","DOI":"10.1109\/GLOCOM.2017.8254900"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1109\/TVT.2016.2543139","article-title":"Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5g wireless communications","volume":"65","author":"Sun","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1109\/TCOMM.2015.2434384","article-title":"Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design","volume":"63","author":"Rappaport","year":"2015","journal-title":"IEEE Trans. Commun."},{"key":"ref_41","first-page":"1","article-title":"Channel model with improved accuracy and efficiency in mmwave bands","volume":"1","author":"Rappaport","year":"2017","journal-title":"IEEE 5G Tech Focus"},{"key":"ref_42","unstructured":"Sun, C., Song, M., Hong, S., and Li, H. (2020). A review of designs and applications of echo state networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3524611","DOI":"10.1109\/TIM.2021.3111009","article-title":"Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network","volume":"70","author":"Catelani","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Peng, H., Chen, C., Lai, C.-C., Wang, L.-C., and Han, Z. (2019, January 11\u201313). A predictive on-demand placement of uav base stations using echo state network. Proceedings of the 2019 IEEE\/CIC International Conference on Communications in China (ICCC), Changchun, China.","DOI":"10.1109\/ICCChina.2019.8855868"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ferreira, A.A., and Ludermir, T.B. (2009, January 14\u201319). Genetic algorithm for reservoir computing optimization. Proceedings of the 2009 International Joint Conference on Neural Networks, Atlanta, GA, USA.","DOI":"10.1109\/IJCNN.2009.5178654"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"B\u00e4ck, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, Inc.","DOI":"10.1093\/oso\/9780195099713.001.0001"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TWC.2019.2942929","article-title":"Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks","volume":"19","author":"Chen","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_48","first-page":"164","article-title":"The ARMA Point Process and its Estimation","volume":"24","author":"Schatz","year":"2022","journal-title":"Econom. Stat."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/12\/4\/58\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:18:22Z","timestamp":1760127502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/12\/4\/58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["jsan12040058"],"URL":"https:\/\/doi.org\/10.3390\/jsan12040058","relation":{},"ISSN":["2224-2708"],"issn-type":[{"type":"electronic","value":"2224-2708"}],"subject":[],"published":{"date-parts":[[2023,7,25]]}}}