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This study analyzes three false-sensing patterns—NFS (Always-No), YFS, and YNFS—while the fusion center aggregates reports at varying time intervals. A denoising autoencoder mitigates abnormal report effects and noise, producing cleaned soft energy data for machine-learning channel-state classification. Experiments compare decision trees, KNN, neural networks, ensemble methods, Gaussian naive Bayes, and random forests, showing that DAE combined with ensemble classification yields the highest accuracy and strongest F1 and MCC.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/enhanced-sensing-performance-through-the-integration-of-denoising-autoencoder-and-ensembling-techniques/450466/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/enhanced-sensing-performance-through-the-integration-of-denoising-autoencoder-and-ensembling-techniques/450466.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What role do false sensing users play in collaborative spectrum sensing?","Question",{"text":112,"@type":113},"False sensing users provide misleading information to the fusion center to undermine the global decision and gain selfish access to spectrum resources.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which false-sensing user types are studied in this work?",{"text":117,"@type":113},"The study considers three types: No False Sensing (NFS/Always-No), Yes False Sensing (YFS), and Yes/No false sensing (YNFS).",{"name":119,"@type":110,"acceptedAnswer":120},"How does the denoising autoencoder improve sensing reliability?",{"text":121,"@type":113},"It mitigates the impact of abnormal sensing reports and noise disturbances at the fusion center, producing cleaned soft energy data for downstream classification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},450466,1791098994,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnhanced sensing performance through the integration of denoising autoencoder andensembling techniques  \nNoor Gul1, Su Min Kim2, Sadiq Akbar1, Atif Elahi3 & Junsu Kim2,4􀀍  \nIn cognitive radio networks (CRNs), collaborative spectrum sensing has emerged as a promising technique for detecting primary user activity. However, the effectiveness of user cooperation is compromised by the presence of malicious users, specifically False Sensing Users (FSUs) . FSUs undermine the effectiveness of collaborative sensing by providing misleading information to the fusion center (FC) in an attempt to selfishly access spectrum resources. Therefore, this study focuses on three types of FSUs that exhibit distinct attack patterns: No False Sensing (NFS, i.e., Always-No), Yes False Sensing (YFS), and Yes/No false sensing (YNFS) users. The FC collects reports from both FSUsand legitimate sensing users at varying time intervals. This study employs a denoising autoencoder (DAE) to enhance sensing reliability by mitigating the effects of abnormal sensing reports and noise disturbances at the FC. While current validation employs synthetic data that closely approximates theoretical CRN conditions, real-world RF validation represents an important direction for future work. The autoencoder produces cleaned soft energy data, which is fed into a machine learning (ML) classifier to estimate channel availability and accumulate global decisions. The present study assesses the effectiveness of various classification techniques, including decision trees (DT), k nearest neighbor (KNN), neural networks (NN), ensemble classification (EC), Gaussian naive Bayes (GNB), and random forest classifier (RFC), to classify channel states. Additionally, this paper aims to provide a comprehensive evaluation of these methods. The integration of DAE and EC yields high accuracy, F1 score, and Matthew’s Correlation Coefficient (MCC), leading to a reliable global decision at the FC with minimal sensing error.  \nKeywords Machine learning, Denoising autoencoder, Loss function, Cognitive radio network, Malicious users  \nTo alleviate the shortage of radio spectrum resources, the Federal Communications Commission (FCC) has sanctioned cognitive radio (CR) technology, which has the potential to improve the efficiency of spectrum utilization1. The term “cognitive” in CR technology is derived from the Latin “cognoscere,” meaning to learn or become aware, reflecting the system’s ability to perceive its environment2. The spectrum sensing phase of the CR process is a critical element that requires user collaboration to achieve optimal performance. However, multipath fading and shadowing effects may lead to reduced sensing accuracy. Two major cooperative schemes available for reliable sensing of the primary user (PU) channel are centralized and distributed. This study focuses on the centralized collaborative approach, wherein a fusion center (FC) collects sensing reports from multiple secondary users to make a global decision3. Recent work4 focuses on optimizing cooperative spectrum sensing (CSS) in energy-harvesting cognitive radio networks (EH-CRNs) . The authors derive the optimal decision threshold for the fusion center (FC) to maximize throughput while also meeting energy and collision constraints. The increasing demand for wireless spectrum, driven by the proliferation of 5G networks and mobile devices, underscores the urgent need for CRNs to efficiently manage spectrum resources5. For EH-CRNs,6 shows that secondary throughput depends critically on balancing sensing energy and data availability. The study further  \n1Department of Electronics, University of Peshawar, Peshawar 25120, KPK, Pakistan. 2Department of Electronics Engineering, Tech University of Korea, Siheung 15073, Gyeonggi-do, Republic of Korea. 3Department of Physics, Higher Education Department, Peshawar 25120, KPK, Pakistan. 4Junsu Kim contribute","cbCaiaLxjHSaZdtj","https://ap.wps.com/l/cbCaiaLxjHSaZdtj","pdf",6057049,"English","# Background and problem\n## Cooperative spectrum sensing and fusion center\n## False sensing users and attack patterns\n# Proposed approach\n## Denoising autoencoder for report cleaning\n## Machine-learning channel state classification\n## Classification methods evaluated","[{\"question\":\"What role do false sensing users play in collaborative spectrum sensing?\",\"answer\":\"False sensing users provide misleading information to the fusion center to undermine the global decision and gain selfish access to spectrum resources.\"},{\"question\":\"Which false-sensing user types are studied in this work?\",\"answer\":\"The study considers three types: No False Sensing (NFS/Always-No), Yes False Sensing (YFS), and Yes/No false sensing (YNFS).\"},{\"question\":\"How does the denoising autoencoder improve sensing reliability?\",\"answer\":\"It mitigates the impact of abnormal sensing reports and noise disturbances at the fusion center, producing cleaned soft energy data for downstream classification.\"}]","Enhanced sensing performance through the integration of denoising autoencoder and ensembling techniques | PDF",1790733267,48]