Interference-Aware Optimization of Three-Tier RIS-Enhanced Hierarchical Aerial Computing: Integrating Terrestrial Base Stations for Persistent 6G IoT Coverage

Interference-Aware Optimization of Three-Tier RIS-Enhanced Hierarchical Aerial Computing: Integrating Terrestrial Base Stations for Persistent 6G IoT Coverage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Interference-Aware Optimization of Three-Tier RIS-Enhanced Hierarchical Aerial Computing: Integrating Terrestrial Base Stations for Persistent 6G IoT Coverage Basma Diaa, Ibrahim I. Ibrahim, Ahmed M. Abd El-Haleem, Mostafa M. Abdelhakam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9193798/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract The proliferation of Internet of Things (IoT) devices in emerging 6G networks demands computing architectures that simultaneously deliver high throughput, low latency, and persistent coverage across heterogeneous deployment environments. Existing two-tier unmanned aerial vehicle–high-altitude platform (UAV–HAP) frameworks offer flexible edge processing but suffer from limited battery endurance, constrained computational capacity, and susceptibility to co-channel interference (CCI) when multiple aerial platforms share the same spectrum. This paper proposes a novel three-tier RIS-enhanced hierarchical aerial computing architecture that integrates a grid-powered reconfigurable intelligent surface–equipped base station (BS-RIS) alongside four RIS-equipped UAVs and a stratospheric HAP, so as to provide persistent, interference-managed 6G IoT coverage. The proposed architecture introduces a sub-array RIS partitioning mechanism in which each RIS panel, consisting of 256 elements divided into 4 sub-arrays, dedicates one sub-array per neighboring interfering platform, achieving 85 % inter-platform interference suppression (residual fraction ψ sup = 0.15). A comprehensive signal-to-interference-plus-noise ratio (SINR) model is derived that captures both intra-platform CCI and inter-platform interference across all tiers. The resulting joint mixed-integer nonlinear programming (MINLP) problem is decomposed into three sequential stages: (i) a three-way hotspot-aware stable matching algorithm that associates IoT devices to platforms while penalising interference-heavy assignments; (ii) a sub-array-aware Riemannian conjugate gradient (RCG) phase optimization that simultaneously enhances desired signal gains and suppresses inter-platform leakage; and (iii) a platform-aware hierarchical task distribution algorithm applying differentiated local-processing thresholds for battery-constrained UAVs (70 % delay margin) versus the grid-powered BS (100 % threshold). Extensive Monte Carlo simulations demonstrate that the proposed framework achieves approximately 30 % higher total computed data volume, 15 % points higher task completion rate, and 20 % lower average end-to-end delay compared to the two-tier UAV–HAP. Physical sciences/Engineering Physical sciences/Mathematics and computing Aerial computing Unmanned aerial vehicle (UAV) High altitude platform (HAP) Reconfigurable intelligent surfaces (RIS) Base station (BS) Mobile edge computing (MEC) Resource allocation Matching game theory Riemannian conjugate gradient (RCG) 6G Internet of Things Co-channel interference (CCI) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9193798","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615504452,"identity":"12e8eba3-da35-4b07-ad7f-3b05cbe34c9b","order_by":0,"name":"Basma Diaa","email":"data:image/png;base64,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","orcid":"","institution":"Capital University (Formerly Helwan University)","correspondingAuthor":true,"prefix":"","firstName":"Basma","middleName":"","lastName":"Diaa","suffix":""},{"id":615504453,"identity":"1d512ad1-f48e-45e4-9d0a-0a4d8a35ae06","order_by":1,"name":"Ibrahim I. 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Existing two-tier unmanned aerial vehicle–high-altitude platform (UAV–HAP) frameworks offer flexible edge processing but suffer from limited battery endurance, constrained computational capacity, and susceptibility to co-channel interference (CCI) when multiple aerial platforms share the same spectrum. This paper proposes a novel three-tier RIS-enhanced hierarchical aerial computing architecture that integrates a grid-powered reconfigurable intelligent surface–equipped base station (BS-RIS) alongside four RIS-equipped UAVs and a stratospheric HAP, so as to provide persistent, interference-managed 6G IoT coverage. The proposed architecture introduces a sub-array RIS partitioning mechanism in which each RIS panel, consisting of 256 elements divided into 4 sub-arrays, dedicates one sub-array per neighboring interfering platform, achieving 85 % inter-platform interference suppression (residual fraction ψ sup = 0.15). A comprehensive signal-to-interference-plus-noise ratio (SINR) model is derived that captures both intra-platform CCI and inter-platform interference across all tiers. The resulting joint mixed-integer nonlinear programming (MINLP) problem is decomposed into three sequential stages: (i) a three-way hotspot-aware stable matching algorithm that associates IoT devices to platforms while penalising interference-heavy assignments; (ii) a sub-array-aware Riemannian conjugate gradient (RCG) phase optimization that simultaneously enhances desired signal gains and suppresses inter-platform leakage; and (iii) a platform-aware hierarchical task distribution algorithm applying differentiated local-processing thresholds for battery-constrained UAVs (70 % delay margin) versus the grid-powered BS (100 % threshold). 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