Large- and Small-Scale Modeling of User Traffic in 5G Networks

Abstract

Along with many other novel features, the fifth generation of mobile networks (5G) aims at highly flexible and dynamic network management, as well as reduced cost for operators. In order to enable both features, rapid and efficient adaptation to environmental changes is needed. This requires a complete knowledge of the characteristics of the user traffic at all time scales, but state-of-the-art research clearly differentiates between large-scale and small-scale traffic behavior. In this work, we propose a traffic model that connects large-scale and smallscale phenomena. We show that the standard small-scale models may produce inaccurate results in case of network congestion. We propose a strategy to mitigate this problem and evaluate it through simulations.

Some Aspects of Blockchain-Enabled Radio Access Networks (B-RAN) Modeling: Review and Theoretical Study

“…For the user traffic models the application of the Gaussian self-similar processes were proposed at [5,7,14] for N>>1 with the parameters calculated from prelimited ON-OFF Pareto models.…”

“…It is worth mentioning, that the problem of the adequate traffic modeling of mobile networks is not new as well! The related works with the most consistent results can be found in the papers [2,3,[6][7][8][9][10][11][12][13] and the book [14]. For the traffic theory of the networks (see, for example [15,16]) all those works use not only Markov modeling but apply as well the theory of self-similar processes for interpretation of the traffic [17] in wireless communication networks of 5G and beyond.…”

“…An important theorem was presented in [5,14], which proofs that for the asymptotic conditions of a large number of users, the self-similar conditions of the on/off Pareto model can be applied considering the following comment: In the scenario considered in [5,7,14] it is assumed a decomposition of the process x(t) (see above), into a sum of independent and identically distributed input flows, which is a unique coincidence (!) with the four stages B-RAN service processing presented above (see [3] ).…”

“…The mean and variance of the Gaussian distribution were obtained in [7] and the simplified expressions are 1 :…”

Delay bound estimation for multicarrier 5G systems considering lognormal beta traffic envelope and stochastic service curve

“…16,17 One can cite the Poisson and autoregressive models that present short-range dependence (SRD) and ignores the long-range dependency of the traffic fluctuation that is present in many communication networks, being inaccurate to model network traffic with multiple scaling properties. 18 Multifractal models precisely describe traffic flows in small scales (ms or smaller), being adequate for the 5G systems, whose scheduling time is of the order of 1 ms. 3,15 Monofractal models, such as the fractional Brownian motion (fBm) and Brownian motion (Bm) models can capture the self-similarity and long-range dependence at a large time scale of the network traffic, although its performance is degraded when applied at small scales. 11 Network traffic analysis in the literature suggests the superiority of multifractal models over monofractals to describe a highly irregular local structure with more complex scale behavior.…”