[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118567-en":3,"doc-seo-118567-105":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},118567,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Channel Estimation for Reconfigurable Intelligent Surface-aided 6G NOMA Systems - A Quantum Machine Learning Approach","Integration of reconfigurable intelligent surfaces (RISs) with non-orthogonal multiple access (NOMA) targets higher spectral efficiency and stronger connectivity for future 6G networks. Accurate channel estimation remains difficult because RIS and NOMA jointly increase system complexity. Although quantum machine learning (QML) has shown promise in wireless, channel-estimation applications are still limited. This work proposes a hybrid quantum-classical model combining CNNs for spatial feature extraction with QLSTM networks for temporal dependencies in time-varying channels, evaluated across power allocation, RIS size, and SNR.","Channel Estimation for Reconfigurable Intelligent Surface-aided 6G NOMA Systems: A Quantum Machine Learning Approach  \nNhien Q. T. Thoong, Graduate Student Member, IEEE, Adnan A. Cheema, Senior Member, IEEE, Berk Canberk, Senior Member, IEEE, Dung Thanh Tran, Graduate Student Member, IEEE, Octavia A. Dobre,  \nFellow, IEEE, Trung Q. Duong, Fellow, IEEE  \nAbstract—The integration of reconfigurable intelligent surfaces (RISs) and non-orthogonal multiple access (NOMA) is considered a promising technique to enhance spectral efficiency and connectivity in future 6G networks. Accurate channel estimation remains a critical challenge in RIS-NOMA systems due to the increased complexity introduced by the combination of RISand NOMA technologies. While quantum machine learning (QML) has demonstrated potential in wireless communications, its application in channel estimation remains underexplored. This paper investigates the effectiveness of a hybrid quantum-classical machine learning (ML) model for channel estimation in RISNOMA systems. We propose a hybrid architecture that integrates convolutional neural networks (CNNs) with quantum long shortterm memory (QLSTM) networks, where CNNs perform spatial feature extraction while QLSTMs capture temporal dependencies in the time-varying channel. Extensive simulations are conducted to evaluate the performance of the model under various network configurations, considering different power allocation factors, the number of RIS elements, and signal-to-noise ratios (SNRs). The performance of the proposed model is benchmarked against both pure quantum and classical ML models, including a quantum neural network (QNN), a CNN, a long short-term memory (LSTM) model, a bidirectional LSTM (BiLSTM) model, and a CNN-LSTM model. The results demonstrate that the proposed CNN-QLSTM model outperforms all baseline methods in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These findings  \nN. Q. T. Thoong and O. A. Dobre are with the Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada (e-mail: qtnthoong, [odobre@mun.ca](odobre@mun.ca)).  \nA. A. Cheema is with the School of Engineering, Ulster University, BT15 1AP Belfast, U.K. (e-mail: [a.cheema@ulster.ac.uk](a.cheema@ulster.ac.uk)) .  \nB. Canberk is with the School of Computing, Engineering and Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, U.K. (email: [b.canberk@napier.ac.uk](b.canberk@napier.ac.uk)).  \nD. T. Tran is with Duy Tan University, Da Nang 50000, Vietnam  \nT. Q. Duong is with the Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1C 5S7, Canada, and also with the School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT7 1NN Belfast, U.K. (e-mail: [tduong@mun.ca](tduong@mun.ca)).  \nThis paper has been accepted in part for presentation at the 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), October 2024, Washington, DC, USA, DOI: 10.1109/VTC2024-Fall63153.2024.10757552 . This work was supported in part by the Canada Excellence Research Chair (CERC) Program CERC-2022-00109, in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program RGPIN-2025-04941, and in part by the NSERC Collaborative Research and Training Experience (CREATE) Program (Grant number 596205-2025) . The work of O. A. Dobre was supported in part by the Canada Research Chair Program CRC-2022-00187 . The work of B. Canberk was partially supported by The Scientific and Technological Research Council of Turkey (TUBITAK) 1515 Frontier R&D Laboratories Support Program for BTS Advanced AI Hub: BTS Autonomous Networks and Data Innovation Lab. Project 5239903 .  \nT. Q. Duong is the corresponding author.  \nhighlight the potential of quantum-enhanced ML for channel estimation in next-generation communication networks.  \nIndex Terms—Channel estima","cbCaiqACh686h0IE","https://ap.wps.com/l/cbCaiqACh686h0IE","pdf",3209118,1,22,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Overview of 6G motivations and enabling technologies\n## Role of NTN and TN for IoE applications","[{\"question\":\"Why is channel estimation challenging in RIS-aided NOMA systems for 6G?\",\"answer\":\"The combination of RIS and NOMA increases system complexity, making accurate channel estimation difficult.\"},{\"question\":\"What hybrid model is proposed for channel estimation?\",\"answer\":\"The approach integrates convolutional neural networks (CNNs) with quantum long short-term memory (QLSTM) networks.\"},{\"question\":\"How does the proposed CNN-QLSTM method perform compared with baseline models?\",\"answer\":\"Simulations show the CNN-QLSTM model achieves the best results across RMSE, MAE, and MAPE against both quantum and classical baselines.\"}]","Channel Estimation for Reconfigurable Intelligent Surface-aided 6G NOMA Systems - A Quantum Machine Learning Approach | PDF",1785684265,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"channel-estimation-for-reconfigurable-intelligent-surface-aided-6g-noma-systems-a-quantum-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/channel-estimation-for-reconfigurable-intelligent-surface-aided-6g-noma-systems-a-quantum-machine-learning-approach/118567/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is channel estimation challenging in RIS-aided NOMA systems for 6G?","Question",{"text":75,"@type":76},"The combination of RIS and NOMA increases system complexity, making accurate channel estimation difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What hybrid model is proposed for channel estimation?",{"text":80,"@type":76},"The approach integrates convolutional neural networks (CNNs) with quantum long short-term memory (QLSTM) networks.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed CNN-QLSTM method perform compared with baseline models?",{"text":84,"@type":76},"Simulations show the CNN-QLSTM model achieves the best results across RMSE, MAE, and MAPE against both quantum and classical baselines.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]