К анализу системы массового обслуживания для сети 5G с технологией NS и приоритетным управлением доступом к радиоресурсам

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Переход к беспроводным сетям пятого поколения 5G ознаменовал новый этап развития информационных и коммуникационных технологий. Сети пятого поколения должны решить такие проблемы, как негибкость «традиционных» сетей и нехватка частотных радиоресурсов для качественного предоставления услуг. Предполагается, что, используя эти сети, мобильные операторы смогут значительно расширить спектр услуг и обеспечить требуемое качество их предоставления. Для удовлетворения требований к качеству обслуживания ( англ. Quality of Service - QoS) операторам необходимо выполнение «ключевых показателей эффективности» ( англ. Key Performance Indicators - KPI), описанных в стандартах связи. Для этой цели могут быть использованы алгоритмы приоритетного облуживания. В статье рассмотрена модель беспроводной сети 5G, поддерживающая технологию нарезки сети и реализующая управление доступом к сетевым радиоресурсам при помощи введения приоритетов. Изучена работа модели в рамках двух алгоритмов. Проведён сравнительный анализ основных показателей эффективности модели.

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Introduction The advent of new generation 5G networks with their flagship slicing technology have highly influenced the telecommunications sector in the best way. Network operators have now the latitude to manage their assets and therefore, are able to propose new types of services to customers [1]-[3]. Businesses and enterprises can now access network connectivity that fits their specific needs [4]-[6]. 3GPP defines slicing as a technology that offers on shared infrastructures the advantageous option to build fully dedicated logical networks, known as ‘network slices’, with very diverse quality of service (QoS) capabilities and requirements [7], [8]. Normally, meeting QoS requirements and extending capabilities are difficult tasks for network operators who can be helped by monitoring the ‘key performance indicators’ (KPIs) [9]-[12]. Essentially, monitoring the KPIs can allow network operators to significantly reduce service interruptions or even prevent them in the best cases [13], [14]. Since the first release of slicing technology few years ago, the vast majority of researchers, scientists and organizations in the telecommunications industry is focused on developing methods and techniques to flexibly and efficiently share available radio resources within its framework [15]-[19]. In modern wireless networks, one of the possible solutions to meet the QoS requirements is the implementation of priority scheduling algorithms [20]-[23]. Models implementing such algorithms within slicing framework could be described using the mathematical apparatus of retrial queueing theory [24]-[26], where retrial queues, also known as ‘orbits’, can be used to address service’s interruptions problem. In this paper we consider one of the possible models for implementing slicing with priority scheduling algorithms. More precisely, we provide a comparative analysis of model’s performance measures under preemptive and non-preemptive scheduling algorithms. For that we use the mathe- matical apparatus of queueing theory and describe the model as a retrial queueing system coupled with a buffer [27]-[29]. The paper is organized as follows. Section 2 provides the system’s general description and proposes a mathematical model for its construction. Sec- tion 3 suggests formulas to compute the stationary probability distributions under preemptive and non-preemptive scheduling algorithms respectively. Section 4 proposes formulas to calculate the main performance measures un- der each priority scheduling algorithm. Section 5 provides a numerical example of system’s model operation. Section 6 concludes the paper. Mathematical model Let us consider a single server retrial queueing system [25] coupled with a buffer. We assume two types of requests arrival in system according to Poisson process with rates

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Об авторах

К. И. Б. Аду

Российский университет дружбы народов

Автор, ответственный за переписку.
Email: 1042205051@rudn.ru
ORCID iD: 0000-0003-4669-0898

PhD Student at the Department of Applied Probability and Informatics, Faculty of Science

ул. Миклухо-Маклая, д. 6, Москва, Россия, 117198

Е. В. Маркова

Российский университет дружбы народов

Email: markova-ev@rudn.ru
ORCID iD: 0000-0002-7876-2801

Candidate of Physical and Mathematical Sciences, Associate Professor at the Department of Applied Probability and Informatics, Faculty of Science

ул. Миклухо-Маклая, д. 6, Москва, Россия, 117198

Е. А. Жбанкова

Российский университет дружбы народов

Email: 1032202159@rudn.ru
ORCID iD: 0000-0003-2482-4488

MSc student at the Department of Applied Probability and Informatics, Faculty of Science

ул. Миклухо-Маклая, д. 6, Москва, Россия, 117198

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© Аду К.И., Маркова Е.В., Жбанкова Е.А., 2022

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