Towards Energy-Sustainable and Fair 6G: A Hybrid Learning Approach for IRS-Assisted MISO-NOMA Systems

Towards Energy-Sustainable and Fair 6G: A Hybrid Learning Approach for IRS-Assisted MISO-NOMA Systems | 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 Towards Energy-Sustainable and Fair 6G: A Hybrid Learning Approach for IRS-Assisted MISO-NOMA Systems Tudumu Reddi Rani, P. Geetha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9197641/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of intelligent reflecting surfaces (IRS) with non-orthogonal multiple access (NOMA) has emerged as a promising paradigm for 6G wireless systems, enabling spectral efficiency, user fairness, and energy sustainability. This paper presents a theoretical machine learning (ML) framework for resource allocation in IRS-assisted multiple-input single-output NOMA (MISO-NOMA) networks. The system model incorporates an IRS phase-shift matrix and user-specific quality-of-service (QoS) constraints, and the optimization objective is to maximize the weighted sum-rate subject to transmit power, discrete IRS phase resolution, and minimum signal-to-interference-plus-noise ratio (SINR) requirements. To address the non-convexity of the joint beamforming, power allocation, and phase-shift optimization, we propose a hybrid supervised–reinforcement learning approach, where offline supervised pre-training provides near-optimal initialization and online reinforcement learning ensures adaptive refinement under dynamic channel conditions. Theoretical analysis demonstrates significant gains in energy efficiency (up to 28%), Jain’s fairness index (> 0.92), and computational latency reduction (sub-millisecond inference) compared to conventional semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. These results confirm that the proposed ML-based framework not only approaches optimal sum-rate performance but also scales efficiently with large IRS deployments, making it suitable for ultra-reliable low-latency communication (URLLC) and energy-conscious 6G applications. Systems and Networking Intelligent Reflecting Surface (IRS) MISO-NOMA 6G Networks Machine Learning Framework Resource Allocation Energy Efficiency Reinforcement Learning Fairness Index Computational Latency Beamforming Optimization Full Text Additional Declarations The authors declare potential competing interests as follows: Tudumu Reddi Rani and P. Geetha* Electronics and Communication Engineering, Mohan Babu University, Tirupati, India Corresponding Email: [email protected] (P.Geetha) Cite Share Download PDF Status: Posted Version 1 posted 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. 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This paper presents a theoretical machine learning (ML) framework for resource allocation in IRS-assisted multiple-input single-output NOMA (MISO-NOMA) networks. The system model incorporates an IRS phase-shift matrix and user-specific quality-of-service (QoS) constraints, and the optimization objective is to maximize the weighted sum-rate subject to transmit power, discrete IRS phase resolution, and minimum signal-to-interference-plus-noise ratio (SINR) requirements. To address the non-convexity of the joint beamforming, power allocation, and phase-shift optimization, we propose a hybrid supervised\u0026ndash;reinforcement learning approach, where offline supervised pre-training provides near-optimal initialization and online reinforcement learning ensures adaptive refinement under dynamic channel conditions. Theoretical analysis demonstrates significant gains in energy efficiency (up to 28%), Jain\u0026rsquo;s fairness index (\u0026gt;\u0026thinsp;0.92), and computational latency reduction (sub-millisecond inference) compared to conventional semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. 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