[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117497-en":3,"doc-seo-117497-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117497,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning in Communication Systems and Networks","A reprint volume compiling selected research articles focused on machine learning methods for communication systems and networking. The book aggregates work originally published across open-access MDPI journals, covering topics such as channel prediction, reinforcement learning for resource allocation and routing, optical encoding and spectrum sensing, modulation classification, and error-correcting code decoding. It provides editor information, citation guidance for referencing each article independently, and licensing details for reuse under Creative Commons terms.","Published in Journals: Applied Sciences, Sensors,  \nElectronics, Photonics, Journal of Sensor and Actuator Networksand Telecom  \nTopic Reprint  \nMachine Learning in Communication Systems and Networks  \nEdited by  \nYichuang Sun, Haeyoung Lee and Oluyomi Simpson  \n[mdpi.com/topics](mdpi.com/topics)  \nMachine Learning in Communication Systems and Networks  \nMachine Learning in Communication Systems and Networks  \nEditors  \nYichuang Sun  \nHaeyoung Lee  \nOluyomi Simpson  \nBasel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester  \nEditors  \nYichuang Sun  \nUniversity of Hertfordshire Hatfield  \nUK  \nHaeyoung Lee University of Hertfordshire Hatfield  \nUK  \nOluyomi Simpson University of Hertfordshire Hatfield  \nUK  \nEditorial Office MDPI  \nSt. Alban-Anlage 66 4052 Basel, Switzerland  \nThis is a reprint of articles from the Topic published online in the open access journals Applied Sciences (ISSN 2076-3417), Sensors (ISSN 1424-8220), Electronics (ISSN 2079-9292), Photonics (ISSN 2304-6732), Journal of Sensor and Actuator Networks (ISSN 2224-2708), and Telecom (ISSN 2673-4001) (available at: [https://www.mdpi.com/topics/ml](https://www.mdpi.com/topics/ml) communication networks).  \nFor citation purposes, cite each article independently as indicated on the article page online and as indicated below:  \nLastname, A.A.; Lastname, B.B. Article Title. Journal Name Year, Volume Number, Page Range.  \nISBN 978-3-7258-0725-3 (Hbk)  \nISBN 978-3-7258-0726-0 (PDF)  \n[doi.org/10.3390/books978-3-7258-0726-0](doi.org/10.3390/books978-3-7258-0726-0)  \n© 2024 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.  \nContents  \nAbout the Editors .............................................. vii  \nPreface .................................................... ix  \nYichuang Sun, Haeyoung Lee and Oluyomi Simpson  \nMachine Learning in Communication Systems and Networks Reprinted from: Sensors 2024, 24, 1925, doi:10.3390/s24061925 .................... 1  \nMohamed Gaballa and Maysam Abbod  \nSimplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System Reprinted from: Sensors 2023, 23, 9010, doi:10.3390/s23219010 .................... 7  \nMohamed Gaballa, Maysam Abbod and Ammar Aldallal  \nA Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System Reprinted from: Sensors 2023, 23, 1383, doi:10.3390/s23031383 .................... 28  \nMario R. Camana, Carla E. Garcia, Taewoong Hwang and Insoo Koo  \nA REM Update Methodology Based on Clustering and Random Forest Reprinted from: Appl. Sci. 2023, 13, 5362, doi:10.3390/app13095362 ................. 56  \nSupachai Phaiboon and Pisit Phokharatkul  \nApplying an Adaptive Neuro-Fuzzy Inference System to Path Loss Prediction in a Ruby Mango Plantation Reprinted from: J. Sens. Actuator Netw. 2023, 12, 71, doi:10.3390/jsan12050071 ........... 73  \nSoheyb Ribouh, Rahmad Sadli , Yassin Elhillali , Atika Rivenq and Abdenour Hadid  \nVehicular Environment Identification Based on Channel State Information and Deep Learning Reprinted from: Sensors 2022, 22, 9018, doi:10.3390/s22229018 .................... 90  \nClayton A. Harper, Mitchell A. Thornton and Eric C. Larson  \nAutomatic Modulation Classification with Deep Neural Networks Reprinted from: Electronics 2023, 12, 3962, doi:10.3390/electronics12183962 ............. 105  \nXiang Zhang and Wei Zhang  \nA Cascade Network for Blind Recognition of LDPC Codes Reprinted from: Electronics 2023, 12, 1979, doi:10.3390/electronics12091979 ............. 127  \nErick Lamilla, Christian Sacarelo, Manuel Alvarez-Alvarado, Arturo Pazmino and Peter Iza  \nOptical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning Reprinted from: Sensors","cbCaiiEMUx4Z9ISG","https://ap.wps.com/l/cbCaiiEMUx4Z9ISG","pdf",41356603,1,400,"English","en",105,"# About the Editors\n# Preface\n# Machine Learning in Communication Systems and Networks\n## Simlified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System\n## A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System\n## A REM Update Methodology Based on Clustering and Random Forest\n## Applying an Adaptive Neuro-Fuzzy Inference System to Path Loss Prediction in a Ruby Mango Plantation\n## Vehicular Environment Identification Based on Channel State Information and Deep Learning\n## Automatic Modulation Classification with Deep Neural Networks\n## A Cascade Network for Blind Recognition of LDPC Codes\n## Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning\n## High Speed Decoding for High-Rate and Short-Length Reed–Muller Code Using Auto-Decoder\n## Sightless but Not Blind: A Non-Ideal Spectrum Sensing Algorithm Countering Intelligent Jamming for Wireless Communication\n## Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks\n## Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning\n## Federated Deep Reinforcement Learning for Joint AeBSs Deployment and Computation Offloading in Aerial Edge Computing Network\n## Beamforming Optimization with the Assistance of Deep Learning in a Rate-Splitting Multiple-Access Simultaneous Wireless Information and Power Transfer System with a Power Beacon\n## Recent Advances in Machine Learning for Network Automation in the O-RAN","[{\"question\":\"What is the scope of this reprint volume?\",\"answer\":\"It compiles research articles on machine learning techniques applied to communication systems and networks, spanning wireless, optical, vehicular, satellite, and network automation contexts.\"},{\"question\":\"Which journals are the reprinted articles originally from?\",\"answer\":\"The reprinted works come from open-access MDPI journals including Applied Sciences, Sensors, Electronics, Photonics, Journal of Sensor and Actuator Networks, and Telecom.\"},{\"question\":\"How should citations be handled for the articles inside the book?\",\"answer\":\"Each article should be cited independently using the journal name, year, volume number, and page range as indicated on the online article page.\"}]","Machine Learning in Communication Systems and Networks | 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