{"paper_id":"4a7d050c-5868-4daf-a476-4ed4b01ac5f1","body_text":"A Novel hybrid GRU based multi-agent D2QL model for enhancing spectrum sensing and resource allocation with Energy Harvesting in Cognitive Radio Networks: Towards Green Communication | 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 A Novel hybrid GRU based multi-agent D2QL model for enhancing spectrum sensing and resource allocation with Energy Harvesting in Cognitive Radio Networks: Towards Green Communication Lingeswari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6385671/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 Energy Harvesting (EH) has become an important facet of sustainable Cognitive Radio Networks (CRNs), helping to improve spectrum efficiency and alleviate Cognitive Users (CU) energy limitations. This research endeavours to enhance critical components of CRNs, encompassing the sensing of spectrum, channel assignment and power distribution, while integrating EH techniques. A multi-agent double deep reinforcement learning methodology utilizing Gated Recurrent Units (GRUs) is proposed to optimize these processes with efficiency. The integration of GRUs substantially enhances the agent’s ability to comprehend and adjust to variable environments by proficiently capturing temporal dependencies within the data. By utilizing a multi-agent framework, the proposed methodology facilitates superior collaboration and decision-making among CUs. The GRU based Multi-Agent double deep Q- learning (MAD2QL) framework is thoroughly tested against single agent deep Q network (SADQN) and conventional deep Q networks (CDQN) with long short term memory (LSTM) based learning methods. This work aims to compare deep reinforcement learning (DRL) models. The double deep learning architecture further augments the stability and convergence of the learning process, resulting in improved performance in EH and resource allocation. Simulation outcomes substantiate that the proposed methodology surpasses existing approaches in terms of sensing efficiency, resource utilization and energy sustainability, thereby presenting a promising solution for the forthcoming generation of CRNs. The mean square error (MSE) for energy consumption of MAD2QL DRL model is 0.26106, 25.7% lower than the MSE of 0.3513 for SADQN. The proposed model achieves 71.59% of EH efficiency. Electrical Engineering Cognitive Radio Networks Double deep Q learning Energy harvesting Gated Recurrent Units Long short term memory Full Text Additional Declarations The authors declare no competing interests. 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. 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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This research endeavours to enhance critical components of CRNs, encompassing the sensing of spectrum, channel assignment and power distribution, while integrating EH techniques. A multi-agent double deep reinforcement learning methodology utilizing Gated Recurrent Units (GRUs) is proposed to optimize these processes with efficiency. The integration of GRUs substantially enhances the agent\\u0026rsquo;s ability to comprehend and adjust to variable environments by proficiently capturing temporal dependencies within the data. By utilizing a multi-agent framework, the proposed methodology facilitates superior collaboration and decision-making among CUs. The GRU based Multi-Agent double deep Q- learning (MAD2QL) framework is thoroughly tested against single agent deep Q network (SADQN) and conventional deep Q networks (CDQN) with long short term memory (LSTM) based learning methods. This work aims to compare deep reinforcement learning (DRL) models. 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