[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125343-en":3,"doc-seo-125343-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},125343,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","ENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS) PREDICTION AND MITIGATION","This innovation leverages machine learning to predict Non-Line-of-Sight (NLOS) conditions in 5G Non-Terrestrial Networks (NTN), improving communication reliability for satellite-based connectivity. Predicted NLOS behavior is dynamically encoded into user equipment (UE) Route Selection Policies (URSPs) to optimize data transfer timing and route selection. The approach focuses on efficient satellite coverage usage while maintaining minimal changes to the existing 5G core infrastructure, enabling more robust performance in discontinuous signal environments.","Technical Disclosure Commons  \nDefensive Publications Series  \n22 Jul 2025  \nENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS) PREDICTION AND MITIGATION  \nAlwin Xavier  \nWojciech Grobel  \nSnezana Mitrovic  \nVinay Saini  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nXavier, Alwin; Grobel, Wojciech; Mitrovic, Snezana; and Saini, Vinay, \"ENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS) PREDICTION AND MITIGATION\", Technical Disclosure Commons,(July 22, 2025)  \n[https://www.tdcommons.org/dpubs_series/8382](https://www.tdcommons.org/dpubs_series/8382)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS)  \nPREDICTION AND MITIGATION  \nAUTHORS:  \nAlwin Xavier  \nWojciech Grobel  \nSnezana Mitrovic  \nVinay Saini  \nABSTRACT  \nThe innovation proposed herein leverages machine learning to predict Non-Lineof-Sight (NLOS) conditions in a 5G Non-Terrestrial Network (NTN), significantly enhancing communication reliability in such networks. By dynamically encoding these predictions into user equipment (UE) Route Selection Policies (URSPs), the innovation proposed herein optimizes data transfers, ensuring efficient use of satellite coverage and improving overall network performance with minimal changes to the existing 5G core infrastructure.  \nDETAILED DESCRIPTION  \nThere are numerous challenges in Third Generation Partnership Project (3GPP) Fifth Generation (5G) Non-Terrestrial Network (NTN) communications that need to be tackled to ensure the success of 5G NTN deployments. During the International Forum on Connecting the World from the Skies, which took place from November 8th to 10th, 2022, in Riyadh, an intriguing study highlighted the critical importance of the line-of-sight (LOS)  \nrequirement in satellite communications.  \nIn satellite communications, the received signal power is significantly diminished under Non-Line-of-Sight (NLOS) conditions. A comparison of received signal strength under NLOS and LOS for Low Earth Orbit (LEO) connected User Equipment (UE), and terrestrial connected devices illustrates the importance of LOS. For example, in NLOS situations, the received signal strength fades dramatically as compared to LOS situations.  \nNLOS refers to scenarios where the communication signal between a satellite anda receiver is obstructed by physical objects such as buildings, trees, mountains, or even the  \n1 8012  \nPublished by Technical Disclosure Commons, 2025 2  \ncurvature of the Earth. Such NLOS scenarios are common in satellite communications.  \nThese random signal strength variations make 5G NTN unreliable for many applications.  \nAn innovative solution is proposed herein that leverages 5G and machine learning  \n(ML) to mitigate these limitations and ensure improved and more efficient data transfers  \nin 5G NTN environments.  \nFrom the perspective of user equipment (UE) and/or Internet of Things (IoT) clientsand servers, a primary challenge in satellite communication is the movement of satellites relative to a UE/IoT client. This movement can cause NLOS conditions due to the curvature of the Earth or other physical obstructions. The NLOS behavior can vary among clients based on the geographical location of each client. Even if a specific area is covered by multiple satellites over time, there is still a chance of experiencing NLOS communication due to the distances between the satellites. Additionally, physical objects near a UE/IoT client can intermittently a","cbCaiqLN0NmQqvXa","https://ap.wps.com/l/cbCaiqLN0NmQqvXa","pdf",916527,1,14,"English","en",105,"# Overview\n## Motivation: LOS dependence and NLOS impact\n## Proposed approach and system goal\n# Detailed Operation\n## Initial connection and packet exchange\n## Latency calculation\n## Machine learning implementation and prediction usage","[{\"question\":\"What problem does the submission address in 5G NTN communications?\",\"answer\":\"It addresses unreliable communication caused by Non-Line-of-Sight (NLOS) signal conditions, which significantly reduce received signal power and vary with satellite movement, geography, and nearby obstructions.\"},{\"question\":\"How does the innovation improve reliability under NLOS conditions?\",\"answer\":\"It uses machine learning to predict when NLOS is likely, then encodes those predictions into UE Route Selection Policies (URSPs) to optimize data transfers and routing toward LOS periods.\"},{\"question\":\"What is the role of the AF and the initial packet exchange?\",\"answer\":\"The Application Function (AF) generates a UDP packet containing the current time and a sequence number, sends it to the client, and receives the client’s response to enable round-trip and uplink latency calculation before ML prediction.\"}]","ENHANCING 5G NON-TERRESTRIAL NETWORK (NTN) COMMUNICATIONS WITH MACHINE LEARNING-DRIVEN NON-LINE-OF-SIGHT (NLOS) PREDICTION AND MITIGATION | 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problem does the submission address in 5G NTN communications?","Question",{"text":75,"@type":76},"It addresses unreliable communication caused by Non-Line-of-Sight (NLOS) signal conditions, which significantly reduce received signal power and vary with satellite movement, geography, and nearby obstructions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the innovation improve reliability under NLOS conditions?",{"text":80,"@type":76},"It uses machine learning to predict when NLOS is likely, then encodes those predictions into UE Route Selection Policies (URSPs) to optimize data transfers and routing toward LOS periods.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of the AF and the initial packet exchange?",{"text":84,"@type":76},"The Application Function (AF) generates a UDP packet containing the current time and a sequence number, sends it to the client, and receives the client’s response to enable round-trip and uplink latency calculation before ML 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