SCIENTIA SINICA Informationis, Volume 47 , Issue 5 : 637(2017) https://doi.org/10.1360/N112017-00044

## The characteristics study of mobile instantaneous messaging traffic in cellular network

• AcceptedMar 20, 2017
• PublishedMay 4, 2017
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### Abstract

Understanding traffic characteristics plays a vital role in network protocol design, which aims to minimize network resource consumption and maximize network stability. In this paper, we use the example of the mobile instantaneous messaging (MIM) service in cellular networks and try to understand its traffic characteristics. Specifically, in order to reach credible conclusions, our research uses practical measurement records from the MIM services of China Mobile at two different levels. First, a dataset of individual message level (IML) traffic is examined and reveals power-law distributed message length and lognormal distributed inter-arrival time. The heavy-tailness of which completely diverts from the geometric and exponential models recommended by 3GPP. Second, another dataset is used to examine the statistical patterns of aggregated traffic within a base station, and demonstrates the accuracy of $\alpha$-stable models for aggregated traffic. Furthermore, we verify that $\alpha$-stable models are suitable for characterizing traffic in both conventional fixed core networks and cellular access networks. Finally, by using the generalized central limit theorem, we build a theoretical relationship between the distributions of IML traffic and aggregated traffic.

### Funded by

• Figure 1

(Color online) The working mechanism of mobile instaneous messaging service in cellular networks

• Figure 2

(Color online) The comparison between heavy-tailed distribution and exponential distribution

• Figure 3

(Color online) The network architecture of GSM/UMTS/LTE co-operating cellular networks

• Figure 4

(Color online) The spatial traffic density of mobile instantaneous messaging service in a random selected region with 23 active base stations. (a) 4:00 a.m.; (b) 10:00 a.m.; (c) 4:00 p.m.; (d) 10:00 p.m.

• Figure 7

(Color online) The modeling results of the aggregated traffic in a randomly selected base station for Wechat

• Figure 8

(Color online) (a)$\sim$(d) The accuracy after fitting the aggregated traffic in four randomly selected base stations by $\alpha$-stable models; (e) the PDF of fitting $\Psi(\omega)$ with respect to $\ln (\omega)$ by using a linear function; (f) the PDF of fitted $\alpha$ values

• Table 1   The modeling accuracy in terms of RMSE for Wechat
 Name PDF Message length Inter-arrival time Aggregated traffic Power-law $ax^{-b}$ 9.76E$-$5 9.25E$-$5 0.0357 Geometric $(1-a)^xa$ 607E$-$5 48.0E$-$5 0.0258 Exponential $a{\rm e}^{-bx}$ 56.0E$-$5 22.9E$-$5 0.0899 Weibull $abx^{b-1}{\rm e}^{-ax^b}$ 65.8E$-$5 8.08E$-$5 0.0470 Lognormal $\frac{1}{\sqrt{2\pi}bx}{\rm e}^{-\frac{(\ln x -a)^2}{2b^2}}$ 34.0E$-$5 7.44E$-$5 0.0491 $\alpha$-stable – 790E$-$5 170E$-$5 0.0144

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