[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85333-en":3,"doc-seo-85333-105":29,"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},85333,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Compound Interference Recognition for LR-FHSS Satellite IoT Uplinks via Multi-Domain Instance Fusion","Long range-frequency hopping spread spectrum (LR-FHSS) enables massive low Earth orbit satellite IoT uplinks where low-power terminals send short packets from wide regions. Satellite links suffer from external interference, and the coexistence of multiple interference components can severely reduce receiver reliability. Existing approaches either target single interference or treat compound combinations as independent classes, limiting generalization and scalability. This work formulates compound interference recognition as multi-instance multilabel learning and proposes a multi-domain instance fusion method combining time-frequency and frequency domains.","Compound Interference Recognition for LR-FHSS Satellite IoT Uplinks via Multi-Domain Instance Fusion  \nHaitao Xu, Boxiang He, Shilian Wang, and Yinan Jiang  \narXiv :2607 . 1 1488v 1 [ cs .IT] 13 Jul 2026  \nAbstract—Long range-frequency hopping spread spectrum (LR-FHSS) is a promising uplink physical layer for massive low Earth orbit satellite Internet of Things, where low power terminals report short packets from wide area regions with limited terrestrial infrastructure. However, satellite IoT links are exposed to external interference, and the coexistence of multiple interference components can severely degrade receiver reliability and complicate interference mitigation. Existing recognition methods either focus on single interference scenarios or treat each compound interference combination as an independent class, leading to limited generalization or poor scalability. To address this problem, this paper formulates LR-FHSS uplink compound interference recognition as a multi-instance multilabel learning problem and proposes a multi-domain instance fusion method. The proposed method fuses local instances from the time-frequency and frequency domains and aggregates their predictions for bag-level multi-label recognition. A dataset construction pipeline is developed based on the US915 LRFHSS configuration and incorporates shadowed-Rician fading and time-varying Doppler to emulate practical satellite communication conditions. Considering the difficulty of obtaining labeled compound interference samples in practice, single-tocompound generalization and few-shot compound interference adaptation are investigated as two practical receiver deployment scenarios. Experimental results show that the proposed method improves the overall exact accuracy over the strongest baseline by 14.71 percentage points in single-to-compound generalization and by 14.81 percentage points in few-shot compound interference adaptation for r = 1.  \nIndex Terms—LR-FHSS, satellite Internet of Things, compound interference recognition, multi-domain instance fusion.  \nI. INTRODUCTION  \nSATELLITE Internet of Things (IoT) extends terrestrial  \nIoT services to remote and infrastructure-limited regions where continuous terrestrial coverage is technically difficult or economically infeasible [1] . Low Earth orbit (LEO) satellites are particularly attractive for direct-to-satellite IoT because their lower altitude reduces link distance and propagation delay compared with higher orbit systems, while broad satellite coverage enables low power terminals to transmit short packets without nearby terrestrial gateways [2], [3] . These properties make LEO satellite IoT suitable for low duty cycle sensing applications that require wide area coverage, including agriculture, environmental monitoring, maritime monitoring, logistics tracking, and utility metering [4], [5] .  \nH. Xu, B. He, S. Wang, and Y. Jiang are with the College of Electronic Science and Technology, National University of Defense Technology, Changsha 410003, P. R. China (e-mail: [HaitaoXu1997@outlook.com](HaitaoXu1997@outlook.com); boxi[anghe1@bjtu.edu.cn](anghe1@bjtu.edu.cn); [wangsl@nudt.edu.cn](wangsl@nudt.edu.cn); [jiangyinan778@168.com](jiangyinan778@168.com)).  \nThis work was supported in part by the National Natural Science Foundation of China under Grant 62501612, and in part by the China Postdoctoral Science Foundation under Grant 2025M774418 .  \nLong range wide area network (LoRaWAN) is a practical candidate for LEO direct-to-satellite IoT with low power terminals, especially for uplink-dominated sensing applications. LoRaWAN regional parameters specify long range-frequency hopping spread spectrum (LR-FHSS) as an uplink physical layer waveform. An LR-FHSS packet consists of repeated physical layer headers and coded payload fragments transmitted over pseudorandom hopping carriers, thereby improving signal coexistence and robustness against narrowband interference in dense IoT deployments [6], [7] . Exis","cbCainmOQdCBjrwl","https://ap.wps.com/l/cbCainmOQdCBjrwl","pdf",2855939,1,13,"English","en",105,"# Introduction\n## Satellite IoT context and LR-FHSS uplink basics\n## Motivation: reliability, security, and compound interference\n## Problem setup: limited labeled compound interference and deployment scenarios\n# Related Works and Gap (implicit)\n## Prior LR-FHSS studies vs. insufficient receiver-side interference recognition","[{\"question\":\"Why is compound interference recognition important for LR-FHSS satellite IoT uplinks?\",\"answer\":\"External interference can degrade outage performance and security, especially when multiple interference components coexist or react over time. Identifying all active components is necessary for effective interference mitigation.\"},{\"question\":\"What limitation of existing recognition methods motivates this paper?\",\"answer\":\"Many methods focus on single interference scenarios or treat each compound interference combination as an independent class, which leads to limited generalization and poor scalability.\"},{\"question\":\"How does the proposed approach represent and learn compound interference?\",\"answer\":\"It formulates LR-FHSS uplink compound interference recognition as a multi-instance multilabel learning problem and uses a multi-domain instance fusion strategy to aggregate bag-level multi-label predictions from fused local instances in time-frequency and frequency domains.\"}]",1784202561,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"compound-interference-recognition-for-lr-fhss-satellite-iot-uplinks-via-multi-domain-instance-fusion","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/compound-interference-recognition-for-lr-fhss-satellite-iot-uplinks-via-multi-domain-instance-fusion/85333/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is compound interference recognition important for LR-FHSS satellite IoT uplinks?","Question",{"text":75,"@type":76},"External interference can degrade outage performance and security, especially when multiple interference components coexist or react over time. Identifying all active components is necessary for effective interference mitigation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation of existing recognition methods motivates this paper?",{"text":80,"@type":76},"Many methods focus on single interference scenarios or treat each compound interference combination as an independent class, which leads to limited generalization and poor scalability.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach represent and learn compound interference?",{"text":84,"@type":76},"It formulates LR-FHSS uplink compound interference recognition as a multi-instance multilabel learning problem and uses a multi-domain instance fusion strategy to aggregate bag-level multi-label predictions from fused local instances in time-frequency and frequency domains.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"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":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]