Model for Estimating the Average Number of Backlogged Users of M2M Systems in 5G Mobile Networks
Abstract
Purpose: The average number of backlogged users is the most important characteristic of Machine-to-Machine communication. A backlogged user is a user with a packet to transmit. A larger number of backlogged users leads to a bigger packet transmission delay. There are solutions for the 5th generation wireless networks which use non-orthogonal resources. This technique allows servicing of a large number of users' devices, providing a relatively small number of backlogged users in the network. The probability of packet detection in a channel depends on the total number of packets transmitted in other resources. This feature complicates the necessary analysis of such systems. The purpose of this work is developing a model for the analysis of Machine-to-Machine systems based on 5th generation wireless networks. Results: A new model of random multiple access has been proposed for the analysis of Machine-to-Machine systems based on 5th generation wireless networks. This model takes into account that the packet detection probability depends on the total number of transmitting users in the system, even if they transmit in orthogonal channels. To analyze such systems, fluid approximation technique was used. With a low computational cost, this technique allows you to estimate such characteristics of Machine-to-Machine systems as packet transmission delays or the average number of backlogged users. The accuracy of the proposed method is demonstrated by a numerical example. Practical relevance: The proposed method and the research results can be used by developers of Machine-to-Machine systems to estimate the average power consumption and transmission delays, including real-time reallocation of the available resources in Machine-to-Machine and Human-to-Human hybrid systems.Published
2015-10-20
How to Cite
Grankin, M. (2015). Model for Estimating the Average Number of Backlogged Users of M2M Systems in 5G Mobile Networks. Information and Control Systems, (5), 72-81. https://doi.org/10.15217/issn1684-8853.2015.5.72
Issue
Section
System and process modeling