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M. Laner, P. Svoboda, N. Nikaein, M. Rupp:
"Traffic Models for Machine Type Communications";
Talk: ISWCS'13, Ilmenau, Germany; 08-26-2013 - 08-30-2013; in: "Proceedings of ISWCS'13", (2013), 5 pages.

Machine-to-machine (M2M) or Machine-type Communication
(MTC) is expected to significantly increase in future
wireless networks. It exhibits considerably different traffic
patterns than human-type communication, thus, claims for
new traffic models and simulation scenarios. The challenge in
designing such models is not only to accurately capture the
behavior of single MTC devices but also to handle their enormous
amount (e.g., up to 30 000 devices per cell) and their coordinated
behavior. Source traffic models (i.e., each device is modeled as
autonomous entity) are generally desirable for their precision
and flexibility. However, their complexity is in general growing
quadratically with the number of devices. Aggregated traffic
models (i.e., all device are summarized to one stream) are far
less precise but their complexity is mainly independent of the
number of devices. In this work we propose an approach which is
combining the advantages of both modeling paradigms, namely,
the Coupled Markov Modulated Poisson Processes (CMMPP)
framework. It demonstrates the feasibility of source traffic
modeling for MTC, being enabled by only linearly growing
complexity. Compared to aggregated MTC traffic models, such
as proposed by 3GPP TR37.868, CMMPP allows for enhanced
accuracy and flexibility at the cost of moderate computational
complexity.

machine-to-machine, traffic model, markov chain, coupling