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Simulation of complex telecommunication systems – running

Current trends in telecommunications prognosticate that mobile and wireless networks will be faced with continuous and massive traffic volume growth in the packet switched domain during 2011-2020 [1]–[3]. To date, this traffic explosion is mostly driven by Internet applications providing almost an unlimited scale of interaction, information, and entertainment services for human users.
As part of the traffic demand evolution thousands of billions of intelligent, resource constrained sensors and other devices are envisioned to be interacting without human intervention in the Internet of Things (IoT) [4], and generating an enormous amount of data for remote controlling, monitoring, measuring, road safety, security/identity checking, or video surveillance functions in Smart Grid [5], Intelligent Transportation Systems [6], mHealth [7] or other advanced application areas. According to recent estimations [8] there could be 225 million mobile and wireless M2M devices by 2014 with infinitesimal traffic per node but resulting significant growth in total, mostly in the uplink direction. The scale of the traffic volume and IoT expansion poses serious research challenges for mobile architectures [9]–[11].
The appaerance of new radio access technologies seems to enable the service of high traffic demands in the radio access part of mobile network. These technologies include:
• LTE, LTE-Advanced technologies,
• hierarchical radiocell coverage of traffic intensive areas (i.e., macro-cell coverage extended with micro, pico and femtocells in areas with bad signal conditions), furthermore
• offload through alternative radio access networks, e.g., WiFi, WiMAX,
However, the scalability of the bachaul transport network and the 3GPP core network functions is an important area where dynamic resource allocation should be ameliorated in order to achieve better utilization. This research path deals among others with the elaboration of distributed core network architecture, improved network management requiring less human intervention, and multi-level traffic management. Network, traffic and resource management functions must enable dynamic ressource allocation and load sharing in the bachaul and core network of internet providers. On the other hand, ISPs must observe the rules of network neutrality [12]. Public authorities require fair service provision from ISPs via content providers and users, independent from the content-type and location. However, congestions will always happen that leads to the blocking or decrease of the traffic of some users. Mobile network providers must explicitly declare in the service level agreements how do they deal with different traffic types in case of congestion situations.
The Evolved Packet Core in 4G (Release 11) has centralized functionalities which may not scale well with the growing network and cause unoptimized traffic routing.
Scalability of the network architecture are meant here both in performance, including green network concepts, and in financial terms. There is certainly a trend that mobile network providers would like to reduce their yearly CAPEX investments for purchasing equipments. This is due to the fatct that under the current traffic evolution and high variability of traffic demand matrices, at the planning phase of purchasing new network equipments the traffic demand forecasts may differ from the demands at the deployment phase, leading to non-appropriate business plans. Cost-efficiency can be improved by allocating resources on-the-need, however this requires technology tha enables on-the-need resource allocation.
By enabling 3GPP backhaul and core networks to work in distributed architecture, the software components can be easily deployed in cloud computing environments / data centers. These could bring benefits to the mobile network provider in terms of CAPEX investments, reliability, and network management.
The goal of this project is to show some techno-economic aspects of changing from centralized to distributed mobile network architectures. Several research questions exit that can be formulated as network dimensioning problems. E.g., what is the maximum cost of a distributed Internet access gateway (GW) which still brings the same CAPEX for the network operator as in case of the centralized architecture? Distribution could be imagined in two ways: using small capacity distributed gateways or by software-defined gateways in a cloud. The CAPEX and OPEX structure of the two cases are completely different.

Currently this research question is under work, and we just made a test run for a network dimensioning problem, that allocates traffic demands to candidate paths in a centralized network architecture supposing real distributed gateways as hardwares. This requires the solution of large mixed integer programming problems.
Applied softwares:
Python 2 – generates the parameters for the network dimensioning problem
AMPL - provides a user-friendly input for integer programming problems
IBM CPLEX – integer programming solver (option 1)
Symphony - integer programming solver (option 2).

[1] Cisco, “Global Mobile Data Traffic Forecast Update, 20112016,” Cisco VNI White Paper, Tech. Rep., Feb 2012.
[2] UMTSForum, “Recognising the Promise of Mobile Broadband,” UMTS Forum White Paper, Tech. Rep., Jul 2010.
[3] Cisco, “The Zettabyte Era,” Cisco VNIWhite Paper, Tech. Rep., May 2012.
[4] G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. D. Johnson, “M2M: From Mobile to Embedded Internet,” IEEE Communications Magazine, vol. 49, no. 4, pp. 36–43, 2011.
[5] X. Fang, S. Misra, G. Xue, and D. Yang, “Smart Grid The New and Improved Power Grid: A Survey,” IEEE Communications Surveys and Tutorials, vol. PP, no. 99, pp. 1–37, 2011.
[6] S. hai An, B.-H. Lee, and D.-R. Shin, “A Survey of Intelligent Transportation Systems,” in Proc. of Third International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), Bali, Indonesia, Jul. 2011, pp. 332–337.
[7] Z. Fan and S. Tan, “M2M Communications for E-Health: Standards, Enabling Technologies, and Research Challenges,” in Proc. of 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, California, USA, Mar. 2012, pp. 1–4.
[8] M. Dohler, T. Watteyne, and J. Alonso-Zrate, “Machine-to-Machine: An Emerging Communication Paradigm,” Tutorial, GlobeCom10, Tech. Rep., Dec 2010.
[9] M. Beale, “Future Challenges in Efficiently Supporting M2M in the LTE Standards,” in Proc. of IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Paris, France, Apr. 2012, pp. 186–190.
[10] Y.-K. Chen, “Challenges and Opportunities of Internet of Things,” in Proc. of 17th Asia and South Pacific Design Automation Conference (ASP-DAC), Sydney, Australia, Jan. 2012, pp. 383–388.
[11] Lioumpas, A. Alexiou, C. Anton-Haro, and P. Navaratnam, “Expanding LTE for Devices: Requirements, Deployment Phases and Target Scenarios,” in Proc. of 11th European Wireless Conference 2011 - Sustainable Wireless Technologies (European Wireless), Vienna, Austria, Apr. 2011, pp. 1–6.
[12] NMHH, „Current Issues of Net Neutrality, Preparatory Document for Public Consultation”, May 2012, URL: http://nmhh.hu/dokumentum/150627/network_neutrality_consultation_document.pdf

Project owner:
Faigl Zoltán (Híradástechnikai Tanszék)
Members:
Híradástechnikai Tanszék (VIK-HIT)
Cooperations:

The results of this project may serve as input to the techno-economical analysis of different network architecture alaternatives in the framework of the Celtic-Plus MEVICO project. (See: http://www.celtic-initiative.org/Projects/Celtic-projects/Call7/MEVICO/mevico-default.asp)

MEVICO.HU project of the Hungarian National Development
Agency (EUREKA Hu 08-1-2009-0043).