Enhancing NOMA User Rates in Smart Train Communication Systems via Joint RIS-IOS Deployment: Cascaded Channel Estimation and Phase Shift Design under Impulsive Noise

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Abstract In this paper, a novel framework is proposed to enhance the performance of power-domain non-orthogonal multiple access (PD-NOMA)-based communication systems in smart trains. The framework leverages the simultaneous deployment of reconfigurable intelligent surface (RIS) and intelligent omni surfaces (IOS) to improve users’ data rates. Due to the presence of impulsive noise arising from both internal and external electromagnetic sources within the train environment—and considering the high sensitivity of PD-NOMA systems to such disturbances—accurate estimation of the cascaded channel comprising both direct and reflected paths among the transmitter, RIS/IOS, and users is of critical importance. To address this, a correntropy-based stochastic gradient ascent (CSGA) algorithm is developed to provide robust channel estimation. Subsequently, a joint phase design strategy for the RIS and IOS is proposed, aiming to maximize the sum rate of PD-NOMA users. Simulation results demonstrate that the CSGA algorithm yields significantly higher channel estimation accuracy compared to the conventional stochastic gradient descent (SGD) approach, resulting in a considerable improvement in the overall system sum rate. The proposed framework, by employing joint phase optimization of RIS and IOS, exhibits strong resilience to impulsive noise and outperforms conventional architectures.
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Enhancing NOMA User Rates in Smart Train Communication Systems via Joint RIS-IOS Deployment: Cascaded Channel Estimation and Phase Shift Design under Impulsive Noise | 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 Research Article Enhancing NOMA User Rates in Smart Train Communication Systems via Joint RIS-IOS Deployment: Cascaded Channel Estimation and Phase Shift Design under Impulsive Noise Masoud Ezzati, Mojtaba Hajiabadi, Hamid Farrokhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7217555/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Wireless Personal Communications → Version 1 posted 14 You are reading this latest preprint version Abstract In this paper, a novel framework is proposed to enhance the performance of power-domain non-orthogonal multiple access (PD-NOMA)-based communication systems in smart trains. The framework leverages the simultaneous deployment of reconfigurable intelligent surface (RIS) and intelligent omni surfaces (IOS) to improve users’ data rates. Due to the presence of impulsive noise arising from both internal and external electromagnetic sources within the train environment—and considering the high sensitivity of PD-NOMA systems to such disturbances—accurate estimation of the cascaded channel comprising both direct and reflected paths among the transmitter, RIS/IOS, and users is of critical importance. To address this, a correntropy-based stochastic gradient ascent (CSGA) algorithm is developed to provide robust channel estimation. Subsequently, a joint phase design strategy for the RIS and IOS is proposed, aiming to maximize the sum rate of PD-NOMA users. Simulation results demonstrate that the CSGA algorithm yields significantly higher channel estimation accuracy compared to the conventional stochastic gradient descent (SGD) approach, resulting in a considerable improvement in the overall system sum rate. The proposed framework, by employing joint phase optimization of RIS and IOS, exhibits strong resilience to impulsive noise and outperforms conventional architectures. Reconfigurable intelligent surface (RIS) intelligent omni surfaces (IOS) correntropy-based stochastic gradient ascent (CSGA) stochastic gradient descent (SGD) PD-NOMA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Wireless Personal Communications → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 25 Jul, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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