Intelligent QoS Optimization in 5G and 6G Networks Using Reinforcement Learning and Shannon Capacity

Intelligent QoS Optimization in 5G and 6G Networks Using Reinforcement Learning and Shannon Capacity | 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 Intelligent QoS Optimization in 5G and 6G Networks Using Reinforcement Learning and Shannon Capacity Hegazi M. Ibrahim, Ibrahim Elewah, Turki M. Alanazi, Magda I. EL-Afifi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9060436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the use of reinforcement learning (RL) techniques to optimize Quality of Service (QoS) parameters in both 5G and 6G wireless networks. A multi-phase experimental framework was employed to evaluate key QoS metrics including latency, jitter, packet loss, throughput, signal-to-noise ratio (SNR), and bit error rate (BER) across both generations. In the first phase, an RL agent was trained in a 5G environment, achieving notable improvements in latency reduction, jitter control, and throughput enhancement. Comparative analysis in the second phase demonstrated that 6G networks consistently outperformed 5G, yielding higher RL rewards, lower packet loss, and reduced jitter and latency. In the final phase, Shannon Capacity theory was integrated into the RL model, further enhancing transmission reliability and signal quality in the 6G context. Additional testing with video streaming scenarios confirmed 6G’s superior capability in supporting real-time, high-reliability applications. Overall, the findings indicate that 6G networks, when optimized with RL, provide a more intelligent, robust, and efficient solution for future wireless communication systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Reinforcement Learning (RL) Quality of Service (QoS) Signal-to-Noise Ratio (SNR) Bit Error Rate (BER) and 6G Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Editor invited by journal 29 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 13 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. 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. 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-9060436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614345096,"identity":"2b6f88ce-1a01-4350-9d5e-6f79ebbecfe8","order_by":0,"name":"Hegazi M. 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A multi-phase experimental framework was employed to evaluate key QoS metrics including latency, jitter, packet loss, throughput, signal-to-noise ratio (SNR), and bit error rate (BER) across both generations. In the first phase, an RL agent was trained in a 5G environment, achieving notable improvements in latency reduction, jitter control, and throughput enhancement. Comparative analysis in the second phase demonstrated that 6G networks consistently outperformed 5G, yielding higher RL rewards, lower packet loss, and reduced jitter and latency. In the final phase, Shannon Capacity theory was integrated into the RL model, further enhancing transmission reliability and signal quality in the 6G context. Additional testing with video streaming scenarios confirmed 6G\u0026rsquo;s superior capability in supporting real-time, high-reliability applications. Overall, the findings indicate that 6G networks, when optimized with RL, provide a more intelligent, robust, and efficient solution for future wireless communication systems.\u003c/p\u003e","manuscriptTitle":"Intelligent QoS Optimization in 5G and 6G Networks Using Reinforcement Learning and Shannon Capacity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 18:11:53","doi":"10.21203/rs.3.rs-9060436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T20:54:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288604663185410974336731708320366751620","date":"2026-05-17T13:41:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30065614959638352045073674419332578203","date":"2026-05-15T09:21:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T05:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43644292427677285912885916078856247989","date":"2026-04-05T08:16:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42560076150132736786029334300431709528","date":"2026-03-30T07:21:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90745480443844098087547940048223394596","date":"2026-03-30T01:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T16:40:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T16:31:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-29T13:37:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T21:07:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-13T11:07:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7057cd2d-3f97-44da-88a1-7f3c01358a01","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T20:54:48+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"288604663185410974336731708320366751620","date":"2026-05-17T13:41:52+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"30065614959638352045073674419332578203","date":"2026-05-15T09:21:49+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65369936,"name":"Physical sciences/Engineering"},{"id":65369937,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-01T18:11:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 18:11:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9060436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9060436","identity":"rs-9060436","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}