[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82771-en":3,"doc-seo-82771-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},82771,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks","Beam hopping-enabled low Earth orbit (LEO) satellites support next-generation communications by delivering global coverage, higher spectrum efficiency, and adaptation to heterogeneous service demands. Achieving this requires dynamic resource allocation and power management, yet onboard limits and adversarial perturbations threaten both efficiency and robustness. The work uses digital twin modeling to capture spatio-temporal user–satellite visibility and provide accurate state information. It builds a joint Markov decision process for beam scheduling and power allocation via the BRIDGE reinforcement-learning method and evaluates robustness under three classical attacks.","Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks  \nShuang Zheng, Xing Zhang, Senior Member, IEEE, Quan Z. Sheng, Haixu Wang, and Wenbo Wang, Senior Member, IEEE  \narXiv :2607 .03859v 1 [ cs .NI] 4 Jul 2026  \nAbstract—Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.  \nIndex Terms—LEO satellite communications, deep reinforcement learning, digital twin, resource allocation, adversarial attack.  \nI. INTRODUCTION  \nWITH the gradual deployment of 5G, research on 6G  \ntechnologies has accelerated worldwide to realize the vision of “global coverage, all spectra, full applications, all senses, all digital, and strong security” [1] . Satellite networks represent a paradigm-shifting breakthrough in communications, overcoming the limitations of traditional terrestrial infrastructure and playing a critical role in supporting the development of 6G [2], [3] . Among various satellite technologies, Low Earth Orbit (LEO) systems have attracted particular attention from both academia and industry due to their low orbital altitude, reduced deployment costs, and broad coverage compared to Geosynchronous (GEO) and Medium  \nThis work is supported by the National Science Foundation of China under Grant 62271062 . The work of Shuang Zheng is supported by China Scholarship Council (CSC) under Grant 202406470029 . (Corresponding authors: Xing Zhang.)  \nShuang Zheng, Xing Zhang, Haixu Wang and Wenbo Wang are with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. Shuang Zheng is also with the School of Computing, Macquarie University, Sydney, NSW 2109, Australia. (e-mail:{zshuang; hszhang; [wbwang](wbwang}@bupt.edu.cn)[}](wbwang}@bupt.edu.cn)[@bupt.edu.cn](wbwang}@bupt.edu.cn)).  \nQuan Z. Sheng is with the School of Computing, Macquarie University, Sydney, NSW 2109, Australia. ([e-mail: michael.sheng@mq.edu.au](e-mail: michael.sheng@mq.edu.au)).  \nEarth Orbit (MEO) satellites [4]–[6] . For example, the SpaceX Starlink constellation already comprises over 8,000 in-orbit satellites [7] . LEO satellites provide low-latency, high-speed communications and efficiently support differentiated services, establishing a crucial foundation for next-generation communications [8], [9] .  \nDriven by rapidly expanding market demands—the global broadband satellite Internet market is projected to grow from approximately $8.1 billion in 2025 to $25.7 billion in 2032 [10]—efficient resource allocation in LEO satellite networks has become increa","cbCaingwM1HB7bcy","https://ap.wps.com/l/cbCaingwM1HB7bcy","pdf",6723808,1,16,"English","en",105,"# Introduction\n## Motivation and background\n## Challenges in differentiated services and robustness\n## Proposed approach overview","[{\"question\":\"Why are beam hopping (BH) LEO satellites important for next-generation communications?\",\"answer\":\"They enable global coverage, improve spectrum efficiency, and support flexible adaptation to heterogeneous service demands by dynamically allocating resources.\"},{\"question\":\"How does the approach use digital twin technology in resource allocation?\",\"answer\":\"It models spatio-temporal dynamics of user–satellite visibility to provide precise state information for decision-making.\"},{\"question\":\"How is robustness evaluated in the proposed framework?\",\"answer\":\"Robustness is systematically tested under three classical adversarial attacks, with results indicating stable performance under bounded adversarial 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