[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122793-en":3,"doc-seo-122793-105":30,"detail-sidebar-cat-0-en-105":92},{"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},122793,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Flexible Payload Configuration for Satellites using Machine Learning","Satellite communications enable connectivity for maritime, aeronautical, and remote regions where terrestrial networks cannot be deployed. Conventional GEO multibeam systems often allocate power and bandwidth uniformly across beams, which becomes inefficient under heterogeneous traffic demands. The paper proposes a machine learning–based radio resource management method by formulating RRM as a regression problem and embedding objectives and constraints directly into the training loss. It also introduces a context-aware metric that evaluates both allocation quality and system-level impact.","Flexible Payload Con􀀂guration for Satellites using  \nMachine Learning  \nMarcele O. K. Mendonc¸a, Flor G. Ortiz-Gomez, Jorge Querol, Eva Lagunas, Juan A. V´asquez Peralvo, Victor Monzon Baeza, Symeon Chatzinotas and Bjorn Ottersten  \nInterdisciplinary Centre for Security Reliability and Trust (SnT) - University of Luxembourg, Luxembourg.  \n[Corresponding Author: marcele.kuhfuss@uni.lu](Corresponding Author: marcele.kuhfuss@uni.lu)  \narXiv :2310 . 11966v1 [ cs .LG] 18 Oct 2023  \nAbstract—Satellite communications, essential for modern connectivity, extend access to maritime, aeronautical, and remote areas where terrestrial networks are unfeasible. Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse. However, recent research reveals the limitations of this approach in heterogeneous traf􀀂c scenarios, leading to inef􀀂ciencies. To address this, this paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM).  \nWe treat the RRM task as a regression ML problem, integrating RRM objectives and constraints into the loss function that the ML algorithm aims at minimizing. Moreover, we introduce a context-aware ML metric that evaluates the ML model’s performance but also considers the impact of its resource allocation decisions on the overall performance of the communication system.  \nIndex Terms—Radio Resource Management, Satellite Communications, Machine Learning.  \nI. INTRODUCTION  \nSatellite networks offer an appealing solution for delivering ubiquitous connectivity across diverse domains such as the maritime and aeronautical marketsand communication services to remote regions [1] . Current Geostationary (GEO) broadband satellite systems use a multibeam footprint strategy to enhance spectrum utilization. In these systems, both power and bandwidth resources are typically allocated uniformly across the various beams. While this uniform allocation simpli􀀂es resource management, it may lead to inef􀀂ciencies in scenarios with varying traf􀀂c demands. Some beams may experience high demand, exceeding their available capacity, while others may have underutilized resources. This challenge has prompted research into more adaptive and dynamic resource allocation methods. In this regard, 􀀃exible payloads have emerged as an enabling technology to manage limited satellite resources by dynamically adapting the frequency, bandwidth, and power of the payload transponders according to users’ demand [2] .  \nExisting approaches aim to minimize the difference between offered and required capacity while adding constraints in terms of power [3], [4], and co-channel interference [5] . The power allocation derived in [3] is solved using water-􀀂lling, whereas a sub-optimal complexity game-based dynamic power allocation (AG-DPA) solution is proposed in [4] . A modi􀀂ed simulated annealing algorithm, as presented in [5], outperforms conventional payload designs in matching requested capacity across beams, emphasizing its effectiveness. However, the intricate computational complexities associated with these algorithms can signi􀀂cantly limit their practical applicability within real-world systems. Moreover, these approaches do not adequately consider the dynamic nature of capacity requests that change over time. In this context, Machine learning (ML) algorithms emerge as a more favorable alternative, as they are able to learn from varying capacity request scenarios.  \nML algorithms have gained popularity in satellite communications, particularly in resource allocation [6] . Some studies explored reinforcement learning (RL) techniques [7] to cope with the time-varying capacity; however, they introduced additional delays due to online payload controller training. Also, the RL exploration phase, aimed at discovering optimal strategies through action exploration, can occasionally result in system outages or disruptions when untested actions are selected. In contr","cbCaij4FrWzmmUbL","https://ap.wps.com/l/cbCaij4FrWzmmUbL","pdf",190383,1,7,"English","en",105,"# Introduction\n## Flexible payloads and RRM motivation\n## Limitations of uniform allocation\n## Related work on optimization and learning\n# System model and problem formulation\n## GEO multibeam scenario\n## RRM task definition\n# ML method and evaluation\n## Regression vs classification approaches\n## Loss design with RRM objectives and constraints\n## Context-aware performance metric\n# Experimental evaluation\n## Results and comparison\n# Conclusion","[{\"question\":\"Why do uniform power and bandwidth allocations become inefficient in GEO multibeam satellites?\",\"answer\":\"Uniform allocation can mismatch heterogeneous traffic demands: some beams may exceed capacity while others remain underutilized, causing inefficiencies.\"},{\"question\":\"How does the paper reformulate radio resource management for machine learning?\",\"answer\":\"It treats the RRM task as a regression problem and integrates RRM objectives and constraints into the loss function optimized during training.\"},{\"question\":\"What is the purpose of the context-aware ML metric proposed in the paper?\",\"answer\":\"The metric evaluates model performance while also accounting for how the model’s resource allocation decisions affect overall communication system performance.\"}]","Flexible Payload Configuration for Satellites using Machine Learning | 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do uniform power and bandwidth allocations become inefficient in GEO multibeam satellites?","Question",{"text":76,"@type":77},"Uniform allocation can mismatch heterogeneous traffic demands: some beams may exceed capacity while others remain underutilized, causing inefficiencies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper reformulate radio resource management for machine learning?",{"text":81,"@type":77},"It treats the RRM task as a regression problem and integrates RRM objectives and constraints into the loss function optimized during training.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the purpose of the context-aware ML metric proposed in the paper?",{"text":85,"@type":77},"The metric evaluates model performance while also accounting for how the model’s resource allocation decisions affect overall communication system 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