804 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 16, NO. 5, JUNE 1998 Spatial Trafﬁc Estimation and Characterization for Mobile Communication Network Design Kurt Tutschku, Student Member, IEEE, and Phuoc Tran-Gia, Member, IEEE Abstract— This paper presents a new method for the estima- use in mobile system engineering. Hence, the demand-based tion and characterization of the expected teletrafﬁc in mobile design of mobile communication systems requires an efﬁcient communication networks. The method considers the teletrafﬁc trafﬁc estimation and characterization procedure which is at from the network viewpoint. The trafﬁc estimation is based on the geographic trafﬁc model, which obeys the geographical the same time both accurate and simple to use. Such a method and demographical factors for the demand for mobile com- will be introduced in the following presentation. munication services. For the spatial teletrafﬁc characterization, The paper is organized as follows. In Section II we in- a novel representation technique is introduced which uses the troduce a new demand-based and integrated mobile network notion of discrete demand nodes. We show how the information planning approach. In Section III we provide ﬁrst an overview in geographical information systems can be used to estimate the teletrafﬁc demand in an early phase of the network de- on trafﬁc source models which are used so far in mobile sign process. Additionally, we outline how the discrete demand network design. In Section III-A we deﬁne a spatial trafﬁc es- node representation facilitates the application of demand-based timation model which takes into account the geographical and automatic mobile network design algorithms. demographical factors for the expected teletrafﬁc in a service Index Terms—Clustering methods, communication system per- region. Subsequently, we introduce the demand node concept, formance, facility locations, land mobile radio cellular system, which is a novel and efﬁcient technique for the representation network planning, trafﬁc estimation. of the spatial distribution of the teletrafﬁc using discrete points. Section IV outlines a trafﬁc characterization procedure I. INTRODUCTION which is capable of deriving a demand node distribution from publicly available geographical data. To generate the demand T HE design of future generation wireless communication networks is facing three major challenges. First, there is a tremendous increase in the demand for mobile communi- nodes, we present a recursive partitional clustering algorithm. Section V demonstrates how the demand node concept can be applied for locating base stations. Section VI summarizes the cation services. Second, due to deregulation acts, competition presentation. between mobile service providers is increased and customers can switch almost instantaneously to the most economical II. DEMAND-BASED SYSTEMATIC provider. And third, new multiplexing and access technologies MOBILE NETWORK PLANNING like space division multiple access (SDMA), code division multiple access (CDMA), or wireless local loop (WLL) require The major drawback of commonly used conventional mo- new network planning methods in order to obtain an efﬁcient, bile network planning methods is their focus on radio fre- economic, and optimal wireless network conﬁguration. quency (RF) aspects. Their main objective is to provide The primary task of mobile system planning is to locate and a sufﬁcient radio signal coverage throughout the complete conﬁgure transmission facilities, i.e., base stations or switching service area, cf. . However, economical aspects of system centers, in the service area of the network and to interconnect deployment and operation are either not addressed effectively these nodes in an optimal way. To achieve an efﬁcient con- or are considered only in a very late stage of the design ﬁguration of these spatial extended systems, new teletrafﬁc process. The handling of this issue is usually left to the models are required to evaluate their spatial performance, cf. expertise of the network design engineer. . In particular, the design of mobile networks has to be The demand-based and integrated approach overcomes this based on the analysis of the distribution of the expected spatial disadvantage by regarding the spatial teletrafﬁc demand dis- teletrafﬁc demand in the complete service area. However, tribution in the service area as a major input factor among most of the trafﬁc models applied so far for the demand the design constraints. The new approach is depicted in estimation characterize the trafﬁc only in a single cell, e.g., Fig. 1. The main cellular design constraints are organized into . Other trafﬁc models, like the highway Poisson-arrival- four equally and parallel considered basic modules, cf. : location model (PALM) proposed by Leung et al. , give radio transmission, mobile subscriber, system architecture, and deep theoretical insights but they are too complex for practical resource management. This structured set of input parameters is used by the integrated concept for the synthesis of a cellular Manuscript received December 1997; revised February 1998. conﬁguration. The system conﬁguration is generated by the u The authors are with Lehrstuhl f¨ r Informatik III, University of automatic network design sequence. u u W¨ rzburg, Am Hubland, W¨ rzburg, D-97074 Germany (e-mail: tutschku@ informatik.uni-wuerzburg.de; firstname.lastname@example.org). In contrast to the conventional design method, the new Publisher Item Identiﬁer S 0733-8716(98)04108-0. approach starts with the analysis of the expected teletrafﬁc 0733–8716/98$10.00 © 1998 IEEE TUTSCHKU AND TRAN-GIA: SPATIAL TRAFFIC ESTIMATION AND CHARACTERIZATION 805 in order to increase system efﬁciency. In Section III-B we examine these types of models in greater detail. A. Trafﬁc Source Models Due to their capability to describe user behavior in detail, trafﬁc source models are usually applied for the characteriza- tion of the trafﬁc in an individual cell of a mobile network. Using these models, local performance measures like new call blocking probability or handover blocking probability can be derived from the mobility pattern. Additionally, these models can be used to calculate subjective quality-of-service values for individual users. Overview on Trafﬁc Source Models: A widely used single cell model was ﬁrst introduced by Hong and Rappaport . Their model assumes a uniformly distributed mobile user density and a nondirected uniform velocity distribution of the Fig. 1. Integrated planning approach. mobiles. Under this premise, performance values like the mean channel holding time and the average call origination rate in a cell can be computed. demand within the considered coverage area, cf. Section V. A more accurate modeling of the calling behavior of users in Due to the equal and parallel contribution of all basic mod- a single cell was proposed by Tran-Gia and Mandjes . The ules, the new concept is capable of obeying the interactions model considers a base station with a ﬁnite customer popula- and dependencies between the design objectives. Hence, the tion and repeated attempts. The appealing characteristic of the capacity and teletrafﬁc engineering objectives can be addressed model is the assumption of a small, ﬁnite user population. This early and in an appropriate way. Moreover, the new ap- is the typical case in real networks, cf. Section IV. However, proach is able to ﬁnd tradeoffs between contrary objectives the model is limited to a single cell and does not consider the like high user coverage and equipment minimization and spatial variation of the teletrafﬁc within the service area. is capable of achieving comprehensive optimized wireless El-Dolil et al.  characterized the mobile phone trafﬁc on network conﬁgurations. Additionally, the approach constitutes vehicular highways by assuming a one-dimensional mobility a forward engineering technique and facilitates the application pattern. They derive the performance values by applying of automatic network design algorithms. a stationary ﬂow model for the vehicular trafﬁc. An ex- tended one-dimensional highway model with a nonuniform III. TRAFFIC ESTIMATION density distribution, denoted as the highway PALM model, was investigated by Leung et al. . For trafﬁc character- In mobile communication networks, the teletrafﬁc originat- ization, ﬂuid ﬂow models with time-nonhomogeneous and ing from the service area of the system can be described by two time-homogeneous trafﬁc have been used, as well as an trafﬁc models which differ in their view of the network. 1) The approximative stochastic trafﬁc model. trafﬁc source model, which is often referred to as the mobility A limited directed two-dimensional mobility model was model, describes the system as seen by the mobile unit. The investigated by Foschini et al. . The model assumes a trafﬁc scenario is represented as a population of individual spatially homogeneous distribution of demand and an isotropic trafﬁc sources performing a random walk through the service mobility structure. Chlebus  investigated a mobility model area and randomly generating demand for resources, i.e., the with a homogeneous demand distribution but which assumes radio channels. An overview of these models is provided in a nonuniform velocity distribution. The trafﬁc orientation is Section III-A. 2) In contrast, the network trafﬁc model of a nondirected and uniformly distributed. mobile communication system describes the trafﬁc as observed The application of these trafﬁc source models in real net- from the nonmoving network elements, e.g., base stations work planning cases is strongly limited. Some models, like the or switches. This model characterizes the spatial and time- highway PALM model, give a deep insight to the impact of ter- dependent distribution of the teletrafﬁc. The trafﬁc intensity minal mobility on cellular system performance; however, they is in general measured in call attempts per time unit and are rather complex to be applied in real network design. Other space unit [calls/(sec km )]. Taking into account the mean models, like the one suggested by Hong and Rappaport , call duration , the offered trafﬁc is (in can only be applied for the determination of the parameters in [Erlang/km ]). This measure represents the amount of offered an isolated cell, due to their simpliﬁcation assumptions. trafﬁc in a deﬁned area. Both trafﬁc models are used in mobile communication system design. The latter model is of principal interest when B. Trafﬁc Intensity determining the location of the main facilities in a mobile Since the cellular network planning process requires a network, i.e., the base stations and the switching centers. These comprehensive view of the expected load and since the trafﬁc components should be located close to the expected trafﬁc source models only focus on a single cell, a network tele- 806 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 16, NO. 5, JUNE 1998 (a) (b) (c) (d) Fig. 2. Demand node concept: (a) geographical and demographical data, (b) trafﬁc matrix, (c) service area tessellation, and (d) demand node distribution. trafﬁc model has to be speciﬁed. Therefore, we deﬁne the The location is a coordinate in , and trafﬁc intensity function This function describes the is the size of the unit area element. number of call requests seen by the ﬁxed network elements Using the assumption that every mobile unit has the same in a unit area element at location during time interval call attempt rate at time the trafﬁc intensity The coordinates of the area element are can be readily obtained integer numbers. Due to the deﬁnition given above, the trafﬁc intensity function is a matrix of trafﬁc values representing the (2) demand from area elements in the service region, cf. Fig. 2(b). The trafﬁc intensity can be derived from the location Since in real world planning cases it is almost impossible probability and the call attempt rate of the mobile units. to directly calculate the location probability from Under the premise that this probability is known, the mobility model, the trafﬁc intensity has to be derived from indirect statistical measures. the average number of mobile units in a certain area element at time is C. The Geographic Network Trafﬁc Model The offered trafﬁc in a region can be estimated by the (1) geographical and demographical characteristics of the service area. Such a demand model relates factors like land use, pop- ulation density, vehicular trafﬁc, and income per capita with Here, is the probability that, if the system is the calling behavior of the mobile units. The model applies viewed from the outside, there is a mobile unit at location statistical assumptions on the relation of trafﬁc and clutter type TUTSCHKU AND TRAN-GIA: SPATIAL TRAFFIC ESTIMATION AND CHARACTERIZATION 807 with the estimation of the demand. In the geographic network usually dropped and the trafﬁc models are reduced to stationary trafﬁc model, the offered trafﬁc is the aggregation models describing the peak trafﬁc. The maximum load is the of the trafﬁc originating from these various factors value of the trafﬁc during the busy hour, cf. . There remains a pitfall for the network designer: the busy (3) hour varies over time within the service area. In downtown areas the highest trafﬁc usually occurs during business hours, whereas in suburban regions the busy hour is expected to be where is the trafﬁc generated by factor in an in the evening. Therefore, the network engineer has to decide arbitrary area element of unit size measured in Erlangs per area how to weight the different trafﬁc factors, i.e., how to obey unit, the number of call attempts per time unit and space the different market shares of various user groups in the trafﬁc unit initiated by factor is the mean call duration of model of the network. calls of type and is the assertion operator trafﬁc factor is not true at location D. Trafﬁc Discretization and Demand Nodes trafﬁc factor is true at location The core technique of the trafﬁc characterization proposed (4) in this paper is the representation of the spatial distribution So far, the planning of public communication systems uses of the demand for teletrafﬁc by discrete points, denoted as geographic trafﬁc models which have a large granularity. In demand nodes. Demand nodes are widely used in economics these cases a typical unit area size is in the order of square for solving facility location problems, cf. . kilometers, i.e., in public cellular mobile systems this is the Deﬁnition: A demand node represents the center of an area size of location areas, cf. . For the determination of the that contains a quantum of demand from teletrafﬁc viewpoint, location of transmission facilities a much smaller value is accounted in a ﬁxed number of call requests per time unit. required. Their locations have to be determined within a spatial The notion of demand nodes introduces a discretization resolution of 100 m. Thus, a unit area element size in the order of the demand in both space and demand. Consequently the of 100 m 100 m is indicated here. demand nodes are dense in areas of high trafﬁc intensity and Trafﬁc Parameters: The values for , which are the trafﬁc sparse in areas of low trafﬁc intensity. Together with the values originating from factor per area element, can be time-independent geographic trafﬁc model, the demand node derived from measurements in an existing mobile network and concept constitutes, in the context of cellular network design, by taking advantage of the known causal connection between a static population model for the description of the subscriber the trafﬁc and its origin. A ﬁrst approach is to assume a distribution. highly nonlinear relationship. A general structure to model An illustration of the demand node concept, given in this behavior is to use a parametric exponential function. In Fig. 2(a), shows publicly available map data with land our proposed geographic model, the trafﬁc-factor relationship use information for the area around the city of W¨ rzburg, u is deﬁned to be Germany. The information was extracted from ATKIS, the ofﬁcial topographical cartographical data base of the Bavarian (5) land survey ofﬁce . The depicted region has an extension of 15 km 15 km. Fig. 2(b) sketches the trafﬁc intensity where is constant and is the base of the exponential distribution in this area, characterized by the trafﬁc matrix: function. dark squares represent an expected high demand for mobile To reduce the complexity of the parameter determination service and bright values correspond to a low teletrafﬁc we introduce the normalization constraint intensity. Fig. 2(d) depicts a simpliﬁed result of the demand discretization. The demand nodes are dense in the city (6) center and on highways, whereas they are sparse in rural areas. In principle, the two-dimensional teletrafﬁc density ma- where is the size of the service area, is trix, cf. Fig. 2(b), is sufﬁcient to characterize the teletrafﬁc the size of a unit area element, and is the total teletrafﬁc distribution in the service area. However, the application of in this region. The value of can be measured in an the demand node representation signiﬁcantly decreases the operating cellular mobile network. computational requirements in network design. Due to the The structure of the geographical trafﬁc model given in (3) use of discrete point representation in mobile system design, and (5) appears to be simple. However, due to its structure the it is no longer necessary to calculate the ﬁeld strength at model can be adapted to the proper trafﬁc parameters. This every point in the area. It is adequate to compute the ﬁeld capability enables its application for mobile system planning. strength values only at the location of the demand nodes, cf. Stationary Geographic Trafﬁc Model: The above proposed Section V. Moreover, the discrete presentation can be used model also includes the temporal variation of the to characterize the clustering effect of users in the service trafﬁc intensity in the service area. Since communication area. The demand node concept enables the evaluation of the systems must be conﬁgured in such a way that they can impact of this user clumping on network performance, cf. accommodate the highest expected load, the time index is . 808 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 16, NO. 5, JUNE 1998 (a) Fig. 4. Algorithm 1: Demand node generation. not usually collected with respect to mobile network planning. (b) (c) For example, ATKIS’s main objective is to maintain map information. It uses a vector format for storing its drawing Fig. 3. Original map information data and data preprocessing: (a) unordered lines, (b) adjacent open and closed polygons, and (c) closing lines. objects. To determine the clutter type of a certain location, one has to identify the land type of the area surrounding this point. IV. TRAFFIC CHARACTERIZATION This requires the detection of the closed polygon describing the shape of this area. Since most maps are printed on paper, A. Trafﬁc Characterization Procedure the order in which the lines of a closed shape are drawn does Based on the estimation method introduced in the previous not matter [see Fig. 3(a)]. To identify closed polygons, one has section, the trafﬁc characterization has to compute the spatial to check if every ending point of a line is the starting point of trafﬁc intensity and its discrete demand node representation another one. If a closed polygon has been detected, the open from realistic data taken from available data bases. In order to lines are removed from the original base and replaced by its handle this type of data, the complete characterization process closed representation. In addition, due to the map nature of comprises four sequential steps. the data, two adjacent area objects can be stored by a closed and an open polygon [see Fig. 3(b)]. Some data might also Step 1) Trafﬁc model deﬁnition: Identiﬁcation of trafﬁc be missing [see Fig. 3(c)]. In this case line closing algorithms factors and determination of the trafﬁc parameters have to applied. After the preprocessing step only closed area in the geographical trafﬁc model. objects remain in the data base and the trafﬁc characterization Step 2) Data preprocessing: Preprocessing of the informa- can proceed with the demand estimation. tion in the geographical and demographical data Trafﬁc Estimation: Step 3) of the trafﬁc characterization base. process uses the geographical trafﬁc model deﬁned in Step Step 3) Trafﬁc estimation: Calculation of the spatial trafﬁc 1) for the estimation of the teletrafﬁc demand per unit area intensity matrix of the service region. element. The computed trafﬁc values are stored in the trafﬁc Step 4) Demand node generation: Generation of the dis- matrix. To obtain the trafﬁc value of a certain unit area crete demand node distribution by the application element, the procedure ﬁrst determines the trafﬁc factors which of clustering methods. are valid for this element and then computes the matrix entry Trafﬁc Model Deﬁnition: The deﬁnition of geographical by applying (3). trafﬁc model in Step 1) of the characterization procedure is based on the arguments given in Section III-C. A simple but accurate spatial geographic trafﬁc model is the basis for B. Demand Node Generation system optimization in the subsequent network design steps. The generation of the demand nodes in Step 4) of the Data Preprocessing: The data preprocessing in Step 2) is characterization process is performed by a clustering method. required since the data in geographical information systems are Clustering algorithms are distinguished into two classes, cf. TUTSCHKU AND TRAN-GIA: SPATIAL TRAFFIC ESTIMATION AND CHARACTERIZATION 809 Fig. 5. ICEPT’s network design sequence. : 1) the partitional clustering methods, which try to con- right-angled bisections, the shape of the tessellation pieces is struct taxonomies between the properties of the data points, always rectangular. To overcome these drawbacks, we also and 2) the hierarchical clustering methods which derive the investigate hierarchical agglomerative clustering algorithms. cluster centers by the agglomeration of input values. These methods are able to obtain tessellation pieces of an The algorithm proposed for the demand generation is a arbitrary shape and of a predeﬁned trafﬁc value. recursive partitional clustering method. It is based on the idea of dividing the service area until the teletrafﬁc of every V. AUTOMATIC CELLULAR NETWORK DESIGN tessellation piece is below a threshold Thus, the algorithm constructs a sequence of bisections of the service region. The A. The ICEPT Planning Tool demand node location is the center of gravity of the trafﬁc weight of the tessellation pieces. To prove the capability of the demand estimation and to show the feasibility of the integrated and systematic design The demand node generation algorithm is shown in Al- concept ICEPT (Integrated Cellular network Planning Tool), a gorithm 1, found in Fig. 4. The function left area() prototype of a planning tool for cellular mobile networks, was divides the area into two rectangles with the same teletraf- u implemented at the University of W¨ rzburg . The tool’s ﬁc and returns the left part of the bisection. The function core components are the automatic network design algorithm right area() returns the right piece. In every recursion SCBPA (set cover base station positioning algorithm) , step the orientation of the partitioning line is turned by 90 . and the trafﬁc characterization procedure as described in The recursion stops if every rectangle represents a trafﬁc Section IV. amount less than the minimal quantization value The func- The SCBPA algorithm is a Greedy heuristic which selects tion traffic() evaluates the amount of expected teletrafﬁc the optimal set of base stations that maximizes the proportion demand in the area. of covered trafﬁc, i.e., the ratio of the demand nodes which An example for the bisection sequence of the algorithm measure a path loss on the forward/reverse link above the is shown in Fig. 2(c). The numbers next to the partitioning threshold of the link budget. lines indicate the recursion depth, and to make the example In the ICEPT prototype, the design constraints and ob- more vivid, not every partition line is depicted. The upper left jectives are implemented as exchangeable modules. These quadrant of Fig. 2(c) only shows lines until the recursion depth modules are indicated in Fig. 5 by the names of the design 3, the lower left only the lines until depth 4, the lower right aspects, e.g., radio wave propagation. ICEPT uses as radio only the lines until depth 5, and the upper right quadrant lines wave propagation models the common Hata model  and until depth 6. the COST231 model . The partitional clustering algorithm of Algorithm 1 is a fast Network Design Sequence: The network design sequence but simple clustering method. However, its accuracy strongly of ICEPT is depicted in Fig. 5. In contrast to the conven- depends on the quantization value which only gives an tional cellular design method, the demand-based approach of upper bound for the trafﬁc represented by a single demand ICEPT starts with the trafﬁc estimation. Therefore, the tool node. Moreover, since the algorithm constructs a sequence of ﬁrst generates the demand node distribution of the planning 810 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 16, NO. 5, JUNE 1998 cation systems. The proposed method considers the teletrafﬁc from the viewpoint of the network. Its trafﬁc estimation is based on the geographic trafﬁc model, which obeys the geographical and demographical factors in the service area for the teletrafﬁc demand estimation. The characterization of the spatial distribution is facilitated by the application of discrete points, denoted as demand nodes. We demonstrated how the demand node pattern can be derived from the information in geographical data bases. In addition, we outlined how the demand node representation enables the application of demand-based automatic mobile network design algorithms. However, since rather insufﬁcient geographical and demo- graphical information often is available in practical cellular system design, it would be of great interest to generate demand node patterns artiﬁcially by the application of spatial point pro- cesses, such as those described by Latouche and Ramaswami , Stoyan and Stoyan , and Cressie . Furthermore, these reference processes could be used to evaluate different planning scenarios. Future applications of the demand node concept are not limited solely to mobile communication systems. The concept Fig. 6. ICEPT planning result: Base station locations. can also be used in other network planning scenarios where transmission facilities have to be deployed in a service area, region. Afterwards it computes the coverage areas for all such as in wireless access networks, wireline data networks, potential transmitter locations and conﬁgurations. In the next or ADSL (asymmetric digital subscriber line) systems . step, ICEPT checks whether or not the trafﬁc and hardware The demand node concept facilitates a revenue-based system constraints are obeyed at these potential sites. Invalid conﬁgu- design. rations are removed and are not considered in the optimization The trafﬁc characterization procedure introduced in step. After completing the veriﬁcation, the SCBPA algorithm Section IV will soon be commercially available as CUTE generates the cellular conﬁguration by selecting a subset of (CUstomer Trafﬁc Estimation tool). More information can transmitter conﬁgurations from the potential set that maxi- be found on the World Wide Web at http://www.infosim- mizes the coverage. During the selection of the transmitters usa.com/CUTE/. the SCBPA algorithm not only considers the demand coverage, but also minimizes the carrier-to-interference (C/I) ratios, cf. ACKNOWLEDGMENT . Subsequently, the tool computes the carrier separation constraints and constructs a frequency allocation plan. If the The authors would like to thank T. Leskien, P. Liebler, frequency allocation plan is valid, ICEPT again veriﬁes C/I and M. Heuler for their valuable programming support and values of the conﬁguration. In the event that the C/I constraints M. Wolfrath, K. Leibnitz, and O. Rose for fruitful discussions are not obeyed, the separation constraints have to be increased. during the course of this work. If the C/I speciﬁcations are met, the network design stops with the output of the cellular radio network conﬁguration. REFERENCES  ATKIS (Amtliches Topographisches Kartographisches Informations Sys- B. Planning Result tem) Bavarian Land Survey Ofﬁce, Munich, Germany, Tech. Rep. 1991.  E. Chlebus, “Analytical grade of service evaluation in cellular mobile ICEPT was tested on the topography around the city center systems with respect to subscribers’ velocity distribution,” in Proc. 8th u of W¨ rzburg. The task was to ﬁnd the optimal locations of Australian Teletrafﬁc Research Seminar, 1993, pp. 90–101. nine transmitters in this terrain. The result of the algorithm is  N. Cressie, Statistic for Spatial Data. New York: Wiley, 1991.  S. A. El-Dolil, W.-C. Wong, and R. Steele, “Teletrafﬁc performance depicted in Fig. 6. The base station locations are marked by of highway microcells with overlay macrocell,” IEEE J. Select. Areas a diamond-shaped symbol The lines indicate the convex Commun., vol. 7, pp. 71–78, Jan. 1989. hull around the set of demand nodes which are supplied by  G. J. Foschini, B. Gopinath, and Z. Miljanic, “Channel cost of mobility,” IEEE Trans. Veh. Technol., vol. 42, pp. 414–424, Nov. 1993. the base station. The SCBPA algorithm was able to obtain a  A. Gamst, E.-G. Zinn, R. Beck, and R. Simon, “Cellular radio network 75% coverage of the teletrafﬁc of the investigated area. The planning,” IEEE Aerosp. Electro. Syst. Mag., vol. 1, pp. 8–11, Feb. 1986.  A. Ghosh and S. L. McLafferty, Location Strategies for Retail and total computing time for the conﬁguration, including the trafﬁc Service Firms. Lexington, MA: Heath, 1987. characterization, was about 4 min on a SUN Ultra 1/170.  S. Grasso, F. Mercuri, G. Roso, and D. Tacchino, “DEMON: A forecasting tool for demand evaluation of mobile network resources,” in Proc. Networks ’96, Sydney, Australia, 1996, pp. 145–150. VI. CONCLUSIONS  M. Hata, “Empirical formula for propagation loss in land mobile radio services,” IEEE Trans. Veh. Technol., vol. 29, pp. 317–325, Aug. 1980. This paper presented a new model for the characterization of  D. Hong and S. S. Rappaport, “Trafﬁc model and performance analysis the expected spatial demand distribution in mobile communi- for cellular mobile radio telephone systems with prioritized and non- TUTSCHKU AND TRAN-GIA: SPATIAL TRAFFIC ESTIMATION AND CHARACTERIZATION 811 prioritized handoff procedures,” IEEE Trans. Veh. Technol., vol. VT-35, u Kurt Tutschku (S’96) was born in W¨ rzburg, Ger- pp. 77–92, Aug. 1986. many, in 1966. He received the Diploma degree  A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Engle- in computer science in 1994 from University of wood Cliffs, NJ: Prentice Hall, 1988. u W¨ rzburg, where he is currently working towards  G. Latouche and V. Ramaswami, “Spatial point patterns of phase type,” the Ph.D. degree. in Proc. 15th Int. Teletrafﬁc Congr., Washington, DC, 1997. From 1991 to 1992 he was a Visiting Student  K. K. Leung, W. A. Massey, and W. Whitt, “Trafﬁc models for wireless at the University of Texas at Austin, TX. In 1994 communication networks,” IEEE J. Select. Areas Commun., vol. 12, pp. he was a Visiting Scientist at the Stuttgart Research 1353–1364, Oct. 1994. Lab of the Daimler-Benz Corporation. In November  K. Maxwell, “Asymmetric digital subscriber line: Interim technology 1994 he became a Member of the Scientiﬁc Staff of for the next forty years,” IEEE Commun. Mag., vol. 34, no. 10, pp. the Department of Distributed Systems at University 100–106, 1996. u of W¨ rzburg. His main ﬁelds of research are planning methods for mobile  M. Mouly and M.-B. Pautet, The GSM System for Mobile Communica- communication systems and high-speed networks and the application of neural tion. Palaiseu, France: self-published, 1992. networks for control of nonlinear dynamic processes.  D. Stoyan and H. Stoyan, Fraktale—Formen—Punktfelder: Methoden der Geometrie-Statistik. Berlin, Germany: Akademie-Verlag, 1992.  G. L. St¨ ber, Principles of Mobile Communication. Boston: Kluwer u Academic, 1996.  P. Tran-Gia and N. Gerlich, “Impact of customer clustering on mobile network performance.” Inst. Comput. Sci., Univ. W¨ rzburg, Germany, u Phuoc Tran-Gia (M’80) received the Dipl.-Ing. Res. Rep. 143, 1996. degree from Stuttgart University and the Dr.-Ing.  P. Tran-Gia and M. Mandjes, “Modeling of customer retrial phenomenon degree from University of Siegen, both in electrical in cellular mobile networks,” IEEE J. Select. Areas Commun., vol. 15, engineering. pp. 1406–1414, Oct. 1997. In 1977 he joined Standard Elektrik Lorenz (now  K. Tutschku, “Demand-based radio network planning of cellular com- Alcatel), Stuttgart, where he worked on the software munication systems,” in Proc. IEEE Infocom ’98, San Francisco, 1998. development of digital switching systems. From  , “Interference minimization using automatic design of cellular 1979 to 1982 he worked in the Department of communication networks,” in Proc. IEEE/VTS 48th Veh. Technol. Conf., Communications, Siegen, and from 1983 to 1986 he Ottawa, Canada, 1998. was with the Institute of Communications Switch-  K. Tutschku, N. Gerlich, and P. Tran-Gia, “An integrated approach to ing and Data Techniques, Stuttgart University. In cellular network planning,” in Proc. 7th Int. Network Planning Symp. 1986 he joined IBM Zurich Research Laboratory where he worked on (Networks ’96), 1996. the architecture and performance evaluation of computer communication  K. Tutschku, K. Leibnitz, and P. Tran-Gia, “ICEPT—An integrated systems. Since 1988 he has been a Professor in the Faculty of Mathematics cellular network planning tool,” in Proc. IEEE/VTS 47th Veh. Technol. u and Computer Sciences, University of W¨ rzburg, Germany. His current Conf., Phoenix, AZ, 1997. research areas include performance modeling of communication systems,  P. E. Wirth, “The role of teletrafﬁc modeling in the new communication planning and optimization of mobile communication networks, and analysis paradigms,” IEEE Commun. Mag., vol. 35, no. 8, pp. 86–93, 1997. of manufacturing systems.
Pages to are hidden for
"Spatial traffic estimation and characterization for mobile communication network design by Tutschku_ K.; Tran-Gia_ P"Please download to view full document