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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Discrete and Continuous Models and Applied Computational Science</journal-id><journal-title-group><journal-title xml:lang="en">Discrete and Continuous Models and Applied Computational Science</journal-title><trans-title-group xml:lang="ru"><trans-title>Discrete and Continuous Models and Applied Computational Science</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2658-4670</issn><issn publication-format="electronic">2658-7149</issn><publisher><publisher-name xml:lang="en">Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">52913</article-id><article-id pub-id-type="doi">10.22363/2658-4670-2026-34-3-288-297</article-id><article-id pub-id-type="edn">RPKOTU</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Computer Science</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Информатика и вычислительная техника</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">A resource queueing model for equal-rate dynamic spectrum slicing in 5G NR networks</article-title><trans-title-group xml:lang="ru"><trans-title>Ресурсная система массового обслуживания для динамической нарезки радиочастот с равной скоростью в сетях 5G NR</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0007-2682-8577</contrib-id><contrib-id contrib-id-type="scopus">58848551900</contrib-id><contrib-id contrib-id-type="researcherid">OKS-6645-2025</contrib-id><name-alternatives><name xml:lang="en"><surname>Ghebrial</surname><given-names>Ibram E. Z.</given-names></name><name xml:lang="ru"><surname>Гебриал</surname><given-names>И. Е. З.</given-names></name></name-alternatives><bio xml:lang="en"><p>Junior Researcher and PhD Student with the Department of Probability Theory and Cyber Security of RUDN University</p></bio><email>gebrial-ie@rudn.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1594-427X</contrib-id><contrib-id contrib-id-type="scopus">35332169400</contrib-id><contrib-id contrib-id-type="researcherid">E-3806-2014</contrib-id><name-alternatives><name xml:lang="en"><surname>Kochetkova</surname><given-names>Irina A.</given-names></name><name xml:lang="ru"><surname>Кочеткова</surname><given-names>И. А.</given-names></name></name-alternatives><bio xml:lang="en"><p>Doctor of Sciences in Physics and Mathematics, Associate Professor with the Department of Probability Theory and Cyber Security of RUDN University; Senior Researcher with the Federal Research Center ``Computer Science and Control'' of the Russian Academy of Sciences</p></bio><email>kochetkova-ia@rudn.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">RUDN University</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Federal Research Center ``Computer Science and Control'' of the Russian Academy of Sciences</institution></aff><aff><institution xml:lang="ru">Федеральный исследовательский центр "Информатика и управление" Российской академии наук</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2026-10-15" publication-format="electronic"><day>15</day><month>10</month><year>2026</year></pub-date><volume>34</volume><issue>3</issue><issue-title xml:lang="en">VOL 34, NO3 (2026)</issue-title><issue-title xml:lang="ru">ТОМ 34, №3 (2026)</issue-title><fpage>288</fpage><lpage>297</lpage><history><date date-type="received" iso-8601-date="2026-10-19"><day>19</day><month>10</month><year>2026</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Ghebrial I.E., Kochetkova I.A.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Гебриал И.Е., Кочеткова И.А.</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Ghebrial I.E., Kochetkova I.A.</copyright-holder><copyright-holder xml:lang="ru">Гебриал И.Е., Кочеткова И.А.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/miph/article/view/52913">https://journals.rudn.ru/miph/article/view/52913</self-uri><abstract xml:lang="en"><p>Background: In fifth-generation (5G) New Radio (NR) network slicing, a mobile network operator leases part of its spectrum to a mobile virtual network operator under service-level agreements. Under equal-rate (Round Robin) scheduling, the base station assigns time-frame shares so that every user in a slice receives the same bitrate regardless of location. Existing analytical queueing frameworks for dynamic spectrum slicing, however, cover only the fixed-power case, and no such framework yet exists for equal-rate scheduling. Purpose: To develop a resource queueing model for dynamic spectrum slicing under equal-rate scheduling, extending the authors' prior fixed-power framework to this regime. Method: We describe each user's channel quality by an inverse spectral efficiency (ISE) and derive its cumulative distribution function from the path-loss model. Under equal-rate scheduling, all users in a slice share a common bitrate equal to the allocated bandwidth divided by the sum of independent, identically distributed ISEs; we characterize this sum exactly through multi-fold convolution and, for computational tractability, approximate it through the central limit theorem. These distributions feed a continuous-time Markov chain (CTMC) that models the joint spectrum partition and per-slice user counts, with state-dependent admission control and stochastic frequency reallocation that preserves isolation guarantees between slices. Results: The framework yields closed-form expressions for the blocking probability, the average per-user bitrate, the average number of active users in each slice, and overall resource utilization. Conclusions: The proposed model provides an analytical tool for spectrum dimensioning and admission control in multi-tenant 5G NR networks under equal-rate scheduling.</p></abstract><trans-abstract xml:lang="ru"><p>Предпосылки: В сетях 5G NR с нарезкой радиочастот оператор мобильной сети предоставляет часть спектра в аренду виртуальному оператору по соглашению об уровне обслуживания. При планировании с равной скоростью (Round Robin) базовая станция распределяет доли временного кадра так, что каждый пользователь слайса получает одинаковую скорость передачи данных независимо от местоположения. Существующие аналитические модели массового обслуживания для динамической нарезки спектра, однако, охватывают только вариант с фиксированной мощностью, и аналогичной модели для планирования с равной скоростью пока не существует. Цель: Построение ресурсной модели массового обслуживания для динамической нарезки спектра при планировании с равной скоростью, расширяющей ранее разработанную авторами схему с фиксированной мощностью на этот режим. Методы: Качество канала каждого пользователя описывается случайной величиной обратной спектральной эффективности, функция распределения которой выводится из модели затухания радиосигнала. При планировании с равной скоростью все пользователи слайса получают общую скорость передачи данных, равную отношению выделенной полосы пропускания к сумме независимых одинаково распределённых величин обратной спектральной эффективности; эта сумма описывается точно через многократную свёртку и для снижения вычислительной сложности приближается с помощью центральной предельной теоремы. Полученные распределения используются при построении непрерывной марковской цепи (CTMC), описывающей совместную динамику распределения спектра и числа пользователей в каждом слайсе, с зависящим от состояния управлением доступом и стохастическим перераспределением частот, сохраняющим изоляцию между слайсами. Результаты: Модель даёт аналитические выражения для вероятности блокировки, средней скорости передачи данных на пользователя, среднего числа активных пользователей в каждом слайсе и общей занятости ресурсов. Выводы: Построенная модель является аналитическим инструментом для расчёта параметров спектра и управления доступом в мультитенантских сетях 5G NR при планировании с равной скоростью.</p></trans-abstract><kwd-group xml:lang="en"><kwd>5G NR</kwd><kwd>CTMC</kwd><kwd>dynamic spectrum management</kwd><kwd>equal-rate scheduling</kwd><kwd>network slicing</kwd><kwd>queueing model</kwd><kwd>Round Robin</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>5G NR</kwd><kwd>CTMC</kwd><kwd>динамическое управление спектром</kwd><kwd>планирование с равной скоростью</kwd><kwd>сетевой слайсинг</kwd><kwd>система массового обслуживания</kwd><kwd>Round Robin</kwd></kwd-group><funding-group/></article-meta><fn-group/></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network Slicing in 5G: Survey and Challenges,” IEEE Communications Magazine, vol. 55, no. 5, pp. 94-100, 2017. 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