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On Radio Resource Allocation Scheme Model with Fixed Transmission Zone for Heterogeneous M2M Traffic in LTE Network

On Radio Resource Allocation Scheme Model with Fixed Transmission Zone for Heterogeneous M2M Traffic in LTE Network

Abstract

На сегодняшний день человека окружает множество технологических устройств (различные датчики контроля, интеллектуальные счётчики и др.), подключение которых к сети изменит традиционное представление об Интернет в целом. Подобные устройства могут осуществлять передачу данных в автоматическом режиме без участия человека, тем самым генерируя трафик межмашинного взаимодействия (англ. M2M, Machine-to-Machine), эффективное обслуживание которого в сетях связи следующего поколения является ещё нерешённой на данный момент задачей. Подключение M2M-устройств к сети предполагает появление множества новых услуг, которые будут привлекательны для пользователя и обеспечат дополнительный доход оператору сети связи. При этом возникает проблема обслуживания возрастающего множества подключённых M2M-устройств, которые передают небольшие объёмы данных. Данная задача является особенно актуальной для сетей мобильной связи LTE (Long Term Evolution), которые исторически были оптимизированы для обслуживания пользователей традиционных услуг связи (англ. H2H, Human-to-Human). Следовательно, требуется разработка новых методов обслуживания трафика нового типа на каждой фазе - от фазы установления соединения до фазы передачи данных. В статье предложена схема динамического распределения радиоресурсов соты сети LTE, когда для обслуживания неоднородного M2M-трафика ресурсы выделяются последовательно диапазонами фиксированного размера. Получено стационарное распределение вероятностей состояний модели и проведён численный анализ.

Today human is surrounded by many technological devices (sensors, smartmeters, etc.) that become connected and will reshape the Internet as we know it today. These devices can transmit and receive data through wireless interfaces transmitting data independently and automatically, thereby generating M2M (Machine-to-Machine) traffic. Efficient service of M2M traffic remains a challenge for future mobile networks. Such massive connectivity offers novel attractive services and provides additional income for operators, but also raises significant challenges to manage large number of devices, typically transmitting only small data fragments. This is especially true for LTE (Long Term Evolution), which has been historically optimized for H2H users (Human-to-Human). Consequently, it is required to develop new methods for M2M traffic for each phase - from the connection establishment phase to the data transmission phase. The article proposes a scheme of dynamic radio resource allocation of LTE cell with fixed transmission zone for heterogeneous M2M traffic. The stationary probability distribution is obtained and the numerical analysis is performed.

Keywords

динамическое распределение радиоресурсов, Machine-to-Machine, диапазон фиксированного размера, elastic traffic, dynamic radioresource allocation, Human-to-Human, fixed transmission zone, LTE, H2H, потоковый трафик, streaming traffic, трафик межмашинного взаимодействия, M2M, эластичный трафик

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green