• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.889-893
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• 1987

This paper suggests a model for the economic evaluation and the selection of alternatives using teletraffic. Economic evaluation is analyzed by the comparison of revenue loss which happens without trunk extension and additional revenue which results from trunk extension. Simulation technique is used as a methodology to apply economic evaluation to telephone system. The study results will provide a support in a optimal decision about investment strategies.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.509-518
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• 2004

Many simulators have been developed and are being used for the complex and various mobile communication service environments. Each of these simulators has its own teletraffic model that consists of traffic source model and network traffic model. In this paper, network traffic model and traffic source model, which are based on the data gathered in real environment, are defined in order to get more accurate simulation results in the mobile communication simulation for the urban region. The network traffic model suggested in this paper reflects the hourly call generation rate and call duration time by analyzing the data collected from actually installed base station by the time and place, and the traffic source model includes the delivery share ratio and average speed information in the region where the base station is installed. This paper defined and designed Mobile Host object that reflects the suggested traffic source model, and Call Generator object that reflects the network traffic model, and other objects support both objects. Using the teletraffic model suggested in the paper, user mobility similar to real service environment and traffic characteristics can be reflected on the simulation, and also more accurate simulation results can be got through that. In addition, by using object-oriented techniques, new service feature or environment can be easily added or changed so that the developed mobile communication simulator can reflect the real service environment all the time.

• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.471-478
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• 2003

Traditionally,, analysis, simulation and measurement have all been used to evaluate the performance of network protocols and functional entities that support mobile wireless service. Simulation methods are useful for testing the complex systems which have the very complicate interactions between components. To develop a mobile call simulator which is used to examine, validate, and predict the performance of mobile wireless call procedures must have the teletraffic model, which is to describe the mobile communication environments. Mobile teletraffic model is consists of 2 sub-models, traffic source and network traffic model. In this paper, we analyzed the network traffic data which are gathered from selected Base Stations (BSs) to define the mobile teletraffic model. We defined 4 types of cell location-Residential, Commercial, Industrial, and Afforest zone. We selected some Base Stations (BSs) which are represented cell location types in Seoul city, and gathered real data from them And then, we present the call rate per hour, cail distribution pattern per day, busy hours, loose hours, the maximum number of call, and the minimum number of calls based on defined cell location types. Those parameters are very important to test the mobile communication system´s performance and reliability and are very useful for defining the mobile network traffic model or for working the existed mobile simulation programs as input parameters.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1373-1382
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• 2001

Traditionally, Mobile Teletraffic model consists of two sub-models, i.e. the network traffic model and the traffic source model. In this paper, we present the traffic source model by developing MobCall(Mobile Call Simulator) which analyses various mobile wireless environments based on regional characteristics that the base stations are located. User mobility is presented by regional average vehicle speeds and the transportation share rate. Moreover, the user mobility on subway, which is increasing in urban area, is considered in MobCall. Using MobCall, the accumulated number of calls in residential and commercial regions, the handoff rate with respect to traffic sources of Seoul, the handoff rate on highway, and the handoff rate according to the call duration are presented. MobCall enables the simulation of dynamic handoff buffering and functional entity control of one base station according to the changes in user's calling pattern at the design phase.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1217-1226
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• 2000

Traditionally, Mobile Teletraffic model consists of two sub-models, i.e. the network traffic model and the traffic source model. In this paper, we present the traffic source model by developing MobCall (Mobile Call Simulator) which analyses various mobile wireless environments based on regional characteristics that the base stations are located. User mobility is presented by regional average vehicle speeds and the transportation share rate. Moreover, the user mobility on subway, which is increasing in urban area, is considered in MobCall. And also, user's movements on highway are considered in MobCall. The object-oriented simulation platform, C++SIM, is used to implement MobCall. Using MobCall, the accumulated number of calls in residential and commercial regions, the handoff rate with respect to traffic sources of Seoul, the handoff rate on highway, and the handoff rate according to the call duration are presented. MobCall enables the simulation of dynamic handoff buffering and functional entity control of one base station according to the changes in user's calling pattern at the design phase. Also, when a new town is under construction by a detailed plan, MobCall is used to design the mobile network with regional characteristics and user mobility considered.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.87-92
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• 2005

This paper presents basic queueing analysis contributing to teletraffc theory, with commonly accessible mathematical tools. This paper studies queueing systems with bulk arrivals. It is assumed that the number of arrivals and the expected number of arrivals in each bulk are bounded by some constraints B and (equation omitted), respectively. Subject to these constraints, convexity argument is used to show that the bulk-size probability distribution that results in the worst mean queue performance is an extremal distribution with support {1, B} and mean equal to A. Furthermore, from the viewpoint of security against denial-of-service attacks, this distribution remains the worst even if an adversary were allowed to choose the bulk-size distribution at each arrival instant as a function of past queue lengths; that is, the adversary can produce as bad queueing performance with an open-loop strategy as with any closed-loop strategy. These results are proven for an arbitrary arrival process with bulk arrivals and a general service model.