Computer Modeling and Simulation of Teletraffic Models for 5G Networks
E.P. Ivanova*, T. Iliev*, Gr. Mihaylov*, I. Stoyanov, * F. Tsvetanov**, E. Otsetova*** and D. Radev *** * University of Ruse, Ruse, Bulgaria ** Southwest University, Blagoevgrad, Bulgaria *** College of Telecommunications and Posts, Sofia, Bulgaria [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]
Abstract - Advances in the new mobile networks are steadily more devices in the whole network. Typically, the increasing the demand on network resources, and new performance of the whole network containing many links standards must ensure that the networks are managed working in parallel and interfering with each other is of effectively in order to ensure that users of the network primary interest. remain satisfied with their Quality of Service (QoS) and Quality of Experience (QoE). The simulations opportunities This type of simulation allows the modeling of represent a chance to reduce the digital divide and to ensure resource management issues, admission of system users the new 5G networks with minimum cost of time and with different priority, interaction and network loading by financials. The simulation of teletraffic models allows the several types of traffic sources, etc. The aim of such modeling of resource management issues, admission of modeling is network capacity evaluation and probabilities system users with different parameters. of failure.
I. INTRODUCTION II. TRENDS OF 5G MOBILE NETWORKS Mobile networks constantly evolve. In the 1980s, The trends of 5G mobile access is to be built upon new when wireless technology began to go mainstream, the 1G radio access technologies, evolving 4G, LTE, HSPA, analog networks supported the bulky, brick-like mobile GSM and WiFi. The new requirements are widely phones from that era. The 90s saw the introduction of 2G varying. The services, devices, number of clients are in digital networks, when Code Division Multiple Access super wide variety. moved mobile phones beyond voice transmission into data The new communication needs of a massive and text messaging. Around 2001, third generation deployment of various devices, such as with low devices appeared, first in Japan, offering the high-speed complexity parameters to more advanced parameters. The data transfer and Internet connection used in the earliest industrial and medicine devices must be lifesaving. The smartphones. The first 4G LTE and WiMAX networks variety of services must be with very low latency and appeared in 2011 [1,7]. Given the natural decade-long probability of blocking. development cycle, fifth generation (5G) networks are due
around 2020. 10 6
Augmented Smart Reality Sensors Simulations represent a good opportunity to reduce the Emergency digital divide and to ensure the new 5G networks. Many Stadium Industry universities and researchers are currently involved in Shopping Logistic HDTV 4 simulation modeling to provide better results. This paper 4G 10 presents an analysis and development of the teletraffic Social 3G 2G models used in three of the Bulgarian universities: Games
University of Ruse, College of Telecommunications and Wireless Vehicular Cloud Office Telematics Virtual Post in Sofia and Southwest University of Blagoevgrad. Reality The implementation of simulation program is made Cloud 3 with .NET. The teletraffic models with geometrical and 2 10 10 Pareto arrival distributions, which are versatile, possessing Real 3D
Multi user UHD 2 Mirror a pseudo-memoryless property which makes the solution Telepressence of many GE-type queuing systems and networks Systems High Speed Train 10 2 Links, km Links, analytically tractable. The new communication systems Delay, ms Throughput, Kbps/km2 are interested in the modeling and performance evaluation 0 9 10 6 10 3 10 of single communication links, but in the modeling of 10 Figure 1. Services of 5G mobile network Project № BG051PO001-3.3.06-0008 “Supporting Academic Development of Scientific Personnel in Engineering and Information Science and Technologies”
MIPRO 2015/CTI 479 The services must be amazingly fast in very dense problems. Applications with high data rates must crowds of users [8]. The fast connections is a key factor be with high security. for success of development of so important services as health and security sectors, fire brigade, high speed trains Shopping mall, traffic jam, concerts, stadium or and cars (Fig.1). The services are: open festivals are places with very high density of users and devices. The expectations are for huge Shopping logistic; amount of aggregated data traffic about up to 900 Gb/km2. The users inside the stadium or concert Vehicular telematics; will generate high quality videos or could transmit Virtual reality; outside the HD movies. The density of users in the shopping mall is high, but the access of broadband Emergency; communications must be fulfilled. Stadium, crowd of people; The necessity of transmitting a huge amounts of data in very short times, very fast connections and priorities of Wireless cloud offices; different services is exact description of arrivals and High speed trains; departures of different clients in described scenarios in 5G. It could be described with Geometrical and Pareto Multiuser UHD telepresence; distributions with heavy load. The high data rates, the HDTV; demand and availability of bandwidth is raising at a much faster rate [2,3,5]. The emergency communication must Mirror Systems; have very low probability of blocking and high reliability. Therefore, the priorities in the model are dynamic. Social games; The teletraffic delay system with two parallel inputs 2G, 3G and 4G services; with Geometrical and Pareto distribution, and one service Smart sensors. output with Geometrical distribution, with finite capacity N was applied with the scenarios of 5G networks (Fig.2). The Quality of Experience requires fully connected users not only for static users. The expectation of The GE is of the form: connectivity is no matter where or how one is moving [8].
Highly mobile devices, e. g. trains and cars, are σt communicating machines for this scenario, but also )tW(P)t(F 1 τ 0,t,e 2 sensors related to widely varying applications, e.g. where τ /( )C and12 σ τν, moving components in industries, plants, other vehicles. where W is a mixed-time random variable of the inter event – time, C2 is the squared coefficient of variation of III. SCENARIOS FOR TELETRAFFIC MODELS FOR 5G W [4]. MOBILE NETWORKS The heavy traffic models must be simulated, because FIFO the various expectations must be fulfilled on very high level. Some scenarios could be described: Priority 1 Emergency communications; traffic efficiency and safety must be with the highest priority of all services and scenarios. When occurs an event as an accident, the devices and the network must be able Geometrical to switch to “emergency mode”, with the highest arrival rate priority, and the battery life must be surviving for a long time. Geometrical Teleprotection in smart grid network will ensure service rate the people with very important messages, which Pareto will be sent between substations for prevention of arrival rate some kind of accident (for example as car crash). Blind spots are the next scenario. The wireless systems have to provide ubiquitous coverage in Priority 2 rural and heaving shadowed areas. The real time remote computing for mobile terminals must ensure the connection. Virtual reality office is the communication point with the necessary of high data rates. The telepresence and communication between the N offices of one company must work without Figure 2. Teletraffic system for 5G mobile network
480 MIPRO 2015/CTI For C2<1, the GE distribution model cannot be service rate, to receive appropriate result. First physically interpreted as a stochastic model. simulations were made with the disciplines of service. For C2>1, the GE model is a mixed-time probability The amount of rare events in system with disciplines of distribution and can be interpreted as bulk time service: LIFO and FIFO are with insignificant difference. distribution with underlying counting process equivalent to compound Poisson process with parameter 2/(C2+1) TABLE I. TELETRAFFIC SYSTEM WITH N=60 Dynamic and geometrically distributed bulk size with mean Service Arrival Arriva Blocking 2 2 n priority (C +1)/2 and squared coefficient of variation (C - rate 1 l rate 2 events rate 1 and 2 1)/(C2+1). 100000 0.5 Pareto 30 30 4 The approximate universal closed form expression for 30000 0.5 30 30 1 GE for blocking probabilities i, i=1,2,…R, can be Pareto determined by using GE-type probabilistic arguments and 0.5 50000 0.5 30 30 1 is described by (2): 10000 0.2 50 40 2 Pareto 10000 0.4 30 60 2 N1 δπ (υ )(1 σ N[ iυ ] (P) υ ), i υ0 i i 0.48 10000 0.3 Pareto 30 60 3
2 10000 0.2 40 60 2 where i() = i, if = 0 or otherwise, σi ai /)C( 21 , 0.44 Pareto 2 10000 0.25 60 60 3 i = ri / (ri(1 - i) + i), and i si /)C(r 21 . 10000 50 40 2 The queuing system has for each class i (i=1,2,…,R, 2 2 30000 50 40 4 R>1) the arrival rate 1/ λi C, si and the service rate 1/ μi C, si . 0.4 50000 0.2 Pareto 50 40 5 The blocking probability is i that an arrival of class I will find in the system at the full capacity [7]. 10000 60 50 1 The blocking probability is determined by arrival 10000 40 60 5 sequence, independently by the rule of service FCFS The priorities in the modeled system are dynamic; the (First Come First Serve), LCFS (Last Come First Serve) service rates of GE are with values 0.2, 0.3, 0.4, 0.44, 0.48 or HOL (Head of Line) static users. and 0.5. The number of examples are 10 000, 30 000, 50 000 and 100 000. The GE service rate is with values from IV. SIMULATION OF TELETRAFFIC MODELS FOR 5G 0.2 to 0.5. Table 1 shows simulation results with N=60 MOBILE NETWORKS places for service in the system, the relative error is 10%, the received blocking events. The results of teletraffic The program for Simulation of Rare events in system with N=90 are described in Table 2. teletraffic systems with single queue is made with the .NET Framework, because it is the way that programming will be done on a Microsoft machine from now. And not TABLE II. TELETRAFFIC SYSTEM WITH N=90 just on a Microsoft machine. The ADO.NET is used for Dynamic Service Arrival Arriva Blocking creating web site, and for manipulating databases. This n priority rate rate 1 l rate 2 events framework create applications for mobile phones and 1 and 2 PDA's with .NET, this is one of the main reasons for using 100000 0.35 60 60 5 it. 10000 0.35 50 60 1 The .NET Framework is a whole lot of Classes (called 50000 0.35 60 60 2 Namespaces) and the technology to get those Classes to work [6]. The main component is called the Common 10000 0.35 40 90 3 0.5 Pareto Language Runtime. With .NET, more than 15 different 30000 0.35 40 90 4 programming languages can use the Common Language Runtime. One of these languages is, of course Visual 50000 0.35 40 90 4 Basic .NET. Another is C#. 100000 0.35 40 90 5
The simulation of the three main types of queuing 10000 0,35 80 60 1 systems is made of the AMD Athlon(tm) II X2 250 Processor 3.00 GHz and 3 GB RAM, running 32-bits 0.4 10000 Pareto 0.1 90 70 5 operation system is Windows 7 Professional. 0.4 30000 Pareto 0.1 90 70 2
First simulations are made with M/M/1/N queuing 0.4 10000 0.5 Pareto 50 80 6 sytems with the .Net tools, because it was necessary to compare analytical and simulation results, to prove and 0.3 30000 45 45 3 validate the new results. The simulation of the queuing 0.3 50000 0.4 Pareto 45 45 4 system is made for different number of observations n. 0.3 10000 45 45 1 The increasing of arrival rate leads to insignificant increasing of blocking events. We choose arrival and 0.2 10000 Pareto 0.1 90 70 1
MIPRO 2015/CTI 481 Dynamic Service Arrival Arriva Blocking The potential users of the simulation product and software n priority rate 1 l rate 2 events could be working groups in the area of the next generation rate 1 and 2 mobile networks. 0.2 0.1 90 40 1
0.2 0.4 Pareto 45 45 1 V. CONLUSION In this paper we have reviewed, presented, validated and discussed the scenarios for teletraffic models for 5G mobile networks. Secondly, this paper presents an implementation of teletraffic models developed in .NET
60 for estimation of blocking events.
50 The simulation results are received for queuing system
40 with Geometrical and Pareto arrivals with two dynamic priorities, the service rate is implemented with 30 Geometrical distribution. The service is organized with 20 B FIFO and a finite buffer size N. 10 The simulation results show that the blocking events in 0 the system decrease with the length of the queues and
-10 increase if the dynamic priorities are increasing.
0 10 90 The future work is the study for the blocking 10 80 0 90 0 probability with RESTART method for with c servers and 7 80 P 0 7 rio 6 0 rit 6 finite capacity N. yS 50 0 e 5 co 0 0 m n 4 4 rea dS 0 St tr 30 3 rst ea 0 Fi m 0 ity 2 20 ior CKNOWLEDGMENT Pr A 10 10 0 0 The present document has been produced with the financial assistance of the European Social Fund under Figure 3. The relationship between dynamic prorities and blocking events B Operational Programme “Human Resources Development”. The contents of this document are the sole On Fig. 3 and Fig.4 are shown the dependences responsibility of “Angel Kanchev” University of Ruse and between system parameters as first and second priorities, can under no circumstances be regarded as reflecting the arrival and service rates and blocking events B. position of the European Union or the Ministry of Education and Science of Republic of Bulgaria: The simulation experiments show that the blocking events in the system decrease with the length of the Project № BG051PO001-3.3.06-0008 “Supporting queues and increase if the dynamic priorities are Academic Development of Scientific Personnel in increasing. Engineering and Information Science and Technologies”.
REFERENCES [1] T. Iliev and Gr. Mihaylov. “3GPP LTE system model analisys and simulation of video transmission”. Proceedings in Advanced Research in Scientific Areas, Slovak Republic, 2012, pp. 2016- 2021. [2] D. Radev, “Modelling of Rare Events in Broadband Digital Networks”, Kolbis, Sofia 2006. (in bulgarian) [3] R.Raev, E. Ivanova, Ek. Dudin and D. Radev. Educational Platform for Teletraffic Engineering. IN: EDULEARN, 2014, Barcelona, 2014, ISBN ISSN 978-84-617-0 [4] E. Ivanova, R. Raev and D. Radev. Simulation of Rare Events in Teletraffic Systems with Single Queue. IN: ICEST, Veliko Tarnovo, 2012 [5] D. Radev, “Teletraffic Engineering”, University publishing center Ruse 2012. (in bulgarian) [6] C. Sells, “Windows Forms Programming in C#”, Addison-Wesley Professional, 2003. [7] D. Koleva and S. Sadinov, “Comparative analysis on the state and development of telecommunication Services, International Scientific Conference UNITECH 2014, Bulgaria, Gabrovo, 2014, pp. 141 - 146 [8] M. Fallgren, B. Timus, P. Popovski, and V. Braun, Deliverable Figure 4. The relationship between arrival rate and service rate and D1.1 “Scenarios, Requirements and KPIs for 5G mobile and blocking events wireless system”, 2013
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