User Distribution and Mobility Model Framework for Cellular Network Simulations Sami Nousiainen, Krzysztof Kordybach, Paul Kemppi VTT Information Technology Tekniikantie 4B, FIN-02044 Espoo, Finland Tel: +358 9 456 4415, email:
[email protected] ABSTRACT Geographical user distribution and mobility have an important effect on cellular network capacity. Dynamic network simulations intended for studying e.g. resource management techniques in cellular networks require flexible and practical models for user distribution and mobility. This paper presents a model that is suitable for implementation and support for many features such as different user classes and street types. Through numerical simulations, we illustrate the effect of some of the features of the mobility model in two scenarios: regular Manhattan scenario and real Helsinki area scenario. I. INTRODUCTION Users’ location and mobility characteristics have a large effect on cellular network performance and capacity. The cellular network planing, encompassing selection of best base station locations, is based on certain assumed offered traffic and geographical user distribution. Especially in CDMA-based UMTS system, the coverage area of the base stations is dependent on the service type and characteristics: high data rate service can be provided only in the vicinity of the base station whereas low data rate services (e.g. speech) are available in a lot larger region. The location area updating and handovers are affected by the mobility characteristics (e.g. movement speed and direction) of the users and the interference situation depends on the users’ location inside the cell. If the users accumulate to a certain hotspot region due to e.g. an entertainment or a sports event or if they prefer a specific movement direction, the situation has to be tackled with algorithms that can take into account these abnormal situations causing congestion. Cellular network simulations can be used to test and analyze the performance under different load situations. It provides a means for analyzing the performance of resource management algorithms. The simulations are required to be dynamic (in contrast to static) to be able to model the mobility of the users and take explicitly into account e.g. the handovers occurring in the network.
In Section II, the background of user distribution and mobility modeling is presented and the previous work is described. In Section III, the proposed and implemented model is explained in detail and reasons for including all the features are given. In Section IV, simulations carried out using the model are described and the results analyzed. Finally, Section V concludes the paper and gives some plans and ideas for future development. II. BACKGROUND AND RELATED WORK Users’ mobility in cellular networks is an important factor that influences e.g. the SIR requirements and radio resource usage through handovers [1]. Higher handover rate due to faster movement of users can be counteracted by resorting to layered network structure where larger macrocells are used to provide capacity to faster users [10]. Smart antennas are another option for providing location and mobility based capacity in the network [8]. However, testing each of these features requires realistic mobility models. In contrast to these cellular network performance issues, where user’s location and mobility influence the performance of the network, the opposite way might also be feasible i.e. the user’s location and mobility are influenced e.g. by regulating the prices and thus providing an incentive for the user to alter his behavior [7]. The analysis of mobility can be carried out on many different levels-of-detail and various requirements on the input data. For our purposes, the cell dwell time level [4] and the analytical approach adopted in [6] represent too low levels-of-detail. Contrary to these approaches, we are rather interested in how to model the user distribution and individual user’s mobility in network simulations by utilizing real maps as input data. An example mobility scenario is provided in [14] separately for urban and vehicular environments. The models serve as a standard point of comparison in UMTS network simulations but, at the same time, they are simple enough to be easily implemented. In [11], [12] and [13], a model comparable to the ETSI vehicular environment mobility model is used in the evaluation of soft handover algorithms. The initial direction of the user is generated from uniform distribution and after an exponentially distributed amount of time the new direction is generated based on
Gaussian distribution and the old direction as the mean. This model does not utilize any topographical information of the region but we have included it in our simulator as a point of comparison. Hierarchical mobility models are presented in [3] and [17]. The former paper describes the mobility on city area, area zone and street unit levels and the latter on international, national and metropolitan levels. As we are aiming at simulating resource management techniques and the consequent network performance, do not want to model the traffic lights or lanes of street because the network simulations would incur high computational burden but, on the other hand, the streets have to be modeled since they constrain the mobility of the users. Mobility modeling of individual users is addressed in [9], [15] and [16]. The activity-based model of [9] requires information about the transitions from activity to another and as an output, we are interested in user’s coordinates not only the sequence of cells in which the user has resided. The treatment in [15] is based on mobility patterns and their characterization with variables such as the number of turns a user makes and the time between turns. In [16], a real raster map defining different structures is used in the mobility model. However, we use a vector map of the streets of the simulation region for constraining the user’s movement because such vector maps are easily available and do not require any additional processing or operations. User distribution and mobility behavior are related to each other since the mobility characteristics modify the user distribution. In [2], the objective has been to find state transition probabilities that would model the mobility and retain the known user distribution. However, our simulator framework enables one to study the effect of mobility on user distribution in real geographical regions with arbitrary initial user distributions. The validation and calibration of a mobility model is a relevant task. Some data affecting the mobility can be easily incorporated into the model. For example, the speed distribution can be based on real speed distribution of moving vehicles. This has been done in [5] where the measured user speed is shown to be approximately distributed according to a truncated Gaussian distribution. III. PROPOSED USER DISTRIBUTION AND MOBILITY MODEL FRAMEWORK A. General The proposed user distribution and mobility model is a compromise between multiple factors. Level of detail and accuracy of modeling, correspondence to reality and computational burden in simulations had to be
considered and assessed. The application of the mobility model in simulations sets some constraints and requirements on the model. Traffic modeling is not addressed in this paper. The call arrival process and the call duration distribution also influence the resulting geographical user distribution but the user distribution and mobility model can also be taken to refer to all users (idle and active mode users together) so that the traffic model has no effect. The traffic model would only determine the changes from between idle and active modes but the mobility of a user is assumed to be independent of the mode. B. Street Network In order to be able to use the model in cellular network simulation in real geographical areas, MapInfo format was chosen as a basis for the input information concerning the street network of the simulation region. The endpoint coordinates of the streets are available in the vector graphics files and type of the street is also given. Maps are available for purchasing in this format and it is possible and easy to generate artificial street networks (e.g. perfect Manhattan-type) in MapInfo format for simulation purposes. The street network can be used to constrain the mobility of the users so that they are allowed to reside only in the streets. Furthermore, because the type of the street is known (highway, main street, etc.), the user class can also stipulate further restrictions to the streets that the user is allowed to reside in. This would emphasize the fact that in the highways, the offered traffic comes from users that are moving with high speeds and thus, handovers are likely to occur more frequently that in other places and attention in resource management should be paid on optimizing the frequent handovers. The width of the street is assumed to depend on the street type as well and this has the effect of accommodating more users to wider streets than to narrower streets. Furthermore, an average speed is associated for each street type because the type of the street is a relevant factor determining the speed of the vehicular users. C. Different User Classes The means of transportation of the cellular network user affects the user’s possible locations and movement speed. Vehicles are not allowed to go to pedestrian streets and pedestrians are not allowed to go to highways. The movement speed of an individual user is determined based on the speed distribution whose parameters (mean and standard deviation of speed) depend on user’s class. The service type is not addressed here but it could depend on users class as well; the percentages of different service types could be given separately for each user class. The user classes chosen for the current mobility model are following
In addition to four normal user classes (stationary, pedestrian, cyclist, vehicular), two additional user classes can be generated to the simulation region. The first one contains stationary users that can reside in any location and the second one users that do not obey the mobility model (streets and turning at crossroads) but move according to the mobility model used in [11], [12], [13] and described previously in this paper and designated as crossing users.
with the line connecting the crossroads and the hotspot is given turning probability photspot and the rest of the streets are given equal turning probabilities of (1– photspot)/n, where n denotes the number of other streets. This models users’ tendency to drift to regions where something is happening (e.g. sports event) and causes the handovers in the network to occur primarily from base stations that are located further away to base stations that are near the event location.
D. User Distribution The initial two dimensional user distribution in the simulation region is given in a matrix. This is given independently of the street network. This approach enables one to provide different user distributions very simply by giving some function for the distribution and calculating the values to a file. The user distribution matrix is then subsequently modified by the street network of the simulation region so that the resulting distribution is zero outside streets. The original distribution matrix is also retained in order to support users that are located outside streets (e.g. inside buildings). The cumulative user distribution matrix is formed from the user distribution matrix and that is used for generating random user locations using inverse transformation method for the cumulative user distribution matrix.
IV. SIMULATIONS
E. Mobility The initial movement direction of the user is chosen based on the side of the street the user’s randomly generated initial location is situated i.e. assuming righthand traffic. The speed of the user depends on two factors: the mean speed of the user class, µuser_class, and the mean speed of the street, µstreet_type. The minimum of these is chosen and the actual user speed generated from a distribution, which is assumed to be Gaussian with standard deviation, σuser_class, depending on user class v user ~ N(min(µ user_class , µ street_type ), σ user_class ) (1)
The location of the user is updated for each time step. The speed of the user is regenerated at each step based on Equation (1) to model the changes in vehicular user’s speed due to braking and accelerating. If the user arrives at a crossroads, the direction is chosen randomly based on turning probabilities at the crossroads. The direction the user comes from is forbidden and all the other turning probabilities depend on many factors. In the simplest case, they can be equal for all directions or unequal probabilities can be provided manually. They can also be altered based on width of the street, which is assumed to depend on the type of street p1 : p 2 : K : p n = w1 : w2 : K : wn (2) where pi is the turning probability for a street and wi is the width of the street. In this case, more user turn to wider streets at the crossroads. The turning probabilities can also be changed based on the location of the user with respect to a given set of hotspot locations. The street that forms smallest angle
A. General The simulations for illustrating the effect of the proposed mobility model on various factors were carried out in two scenarios: in regular Manhattan scenario and a real area of Helsinki. B. Indicators used There are some general level indicators describing the effect of the mobility model. E.g. • cell dwell time distribution (the time the user has connection to a certain base station) • cell load (number of users in a base station) • user distribution as a function of time • users’ trajectories In order to be able to estimate the effect of the mobility model on cell dwell time and cell load, a simplification was made at this stage: the nearest base station was taken to be the one that the user is connected to. The handover process was taken to happen when there was a change in the nearest base station. The cell dwell was thus the time during which the user had a certain base station as nearest base station. C. Simulations with regular Manhattan scenario The parameters used in the simulations in Manhattan scenario are listed in Table 1. The block width and the street width correspond to those proposed in [14]. The total number of base stations was 45 and the base stations were distributed to the simulation area according to the deployment scenario of [14]. Table 1. Parameter values used in simulations related to the Manhattan scenario. Parameter block size street width number of streets number of base stations
Value 200 m × 200 m 30 m 10 × 10 45
The simulations were carried out using 1000 users, all existing from the beginning of the simulation till the end. The duration of the simulations was 180 seconds and the time step of recording data about the users’ movements was 1 second. Two user classes were used in two simulations separately, vehicular users staying in the streets and crossing users able to move anywhere. The speed of the users was first maintained as 40 km/h
for the whole simulation duration and then allowed to vary with variance of 10 km2/h2. Big differences were observed in the distribution of user’s direct distance between the start location and end location. The mobility model and the street network have an effect on the straightness of the user’s trajectory and thus influence the fact whether the user has a high probability of ending up close to the location where he started from. In Figure 1, the cell dwell time distributions for vehicular users with constant speed and with Gaussian distributed speed are shown. The distributions contain some prominent peaks that are caused by the street orientations. The peaks remained from simulation to another when the same parameter values were used. When the user’s speed was allowed to vary, the peaks were wider. However, when only crossing users were used (i.e. those that are not constrained to streets), the distribution did not contain such constant prominent peaks and it varied from run to another more.
simulator, such as traffic model and interference in UMTS, is likely to cause even more variation in the cell load. D. Simulations with Helsinki area In Figure 3, 500 uses are shown in the Helsinki area map. The initial user distribution was such that the probability density function in the middle third of the simulation region was 10 times higher than in the border regions. The non-uniformity of the user distribution can be seen in the map at the initial stage of the simulation (after 11 seconds). However, as a result of the mobility of the users, the user distribution changes considerably and after 15 minutes of simulated time, the user distribution is almost uniform.
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In Figure 4, trajectories for 8 users are shown. The hotspot was placed in a popular public event location in Helsinki (coordinates 3386510, 6674520) and the simulated time was 20 minutes. The trajectories still contain some randomness due to the stochastic nature of the mobility model and street constraints even in the presence of a hotpot but most of the trajectories are clearly directed towards the hotspot. Only one user of the 8 users still remains a bit further away from the hotspot at the end of the simulation. V. CONCLUSION AND FUTURE WORK
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Figure 2. Cell load in one base station as a function of time.
The average number of users in one of the central base stations approximately 20 but the variations already due to mobility are considerable as can be seen in Figure 2 where the cell load for one central base station is shown as a function of time. Incorporating more features to the
The proposed user distribution and mobility model and simulation framework is very flexible and supports many relevant features such as different user classes (pedestrians, vehicles), different street types, street weighing and hotspots. On the other hand, the model is simple and practical enough to be implemented and required data (street network) is available in appropriate format and does not require manual preprocessing. The proposed model can be used in cellular network simulations in real geographical regions. It is also possible to create artificial test scenarios for the
mobility model in the same format as the real maps are obtained. 6.676
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Figure 4. Trajectories of 8 users moving towards a hotspot.
In future, a flexible traffic model should be included in the simulator to model the call arrival and call duration for different service types. This would have an effect on the cell residence time. Also, all the features affecting network performance on the system level such as propagation losses, transmission power control and handovers should be added to the simulator framework in order to be able to model changes in the connections of the user to the base stations. Finally, various resource management algorithms are to be implemented in the simulator and evaluated. New features and amendments can then be implemented to the mobility model if needed. ACKNOWLEDGEMENTS
This work has been partially performed in the framework of the project IST CAUTION, which is partly funded by the European Community. The Authors would like to acknowledge the contributions of their colleagues from National Technical University of Athens, VTT Information Technology, Cosmote Mobile Telecommunications S. A., Telia Mobile and Motorola S.p.A. REFERENCES
[1] Remco Litjens, "The Impact of Mobility on UMTS Network Planning," IEEE Vehicular Technology Conference, Vol. 4, pp. 2539–2543, Spring 2001. [2] Tony Dean, "A method for achieving stable distributions of wireless mobile location in motion simulations," in Proceedings of the 2000 Winter Simulation Conference, 2000. [3] Markoulidakis, Lyberopoulos, Tsirkas, Sykas, "Mobility modeling in third generation mobile telecommunication systems," IEEE Personal Communications, pp. 41–56, Aug. 1997. [4] Jugl, Boche: "Dwell time models for wireless communication systems", Vehicular Technology Conference VTC’99 Fall, Amsterdam, September 1999.
[5] Kobayashi, Shinagawa, Watanabe, "Vehicle mobility characterization based on measurements and its application to cellular communication systems," IEICE Trans. Commun., Vol. E82-B, December 1999. [6] Cho, Jin, Cho, "Comparisons of mobility models in cellular systems", VTC’99 fall. [7] E.D. Fitkov-Norris, A. Khanifar, "Dynamic pricing in cellular networks, a mobility model with a provider-oriented approach," Second International Conference on 3G Mobile Communication Technologies, pp. 63–67, 2001. [8] A. Barakat, D. Everitt, "Performance evaluation and traffic analysis in cellular systems using smart antennas in the presence of mobility," IEEE Vehicular Technology Conference, Vol. 3, pp. 1513–1517, Fall 2001. [9] J. Scourias, T. Kunz, "Activity-based mobility modeling: realistic evaluation of location management schemes for cellular networks," IEEE Wireless Communications and Networking Conference, Vol. 1, pp. 296–300, 1999. [10] G. Cimone, D.D. Weerakoon, A.H. Aghvami, "Performance evaluation of a two layer hierarchical cellular system with variable mobility users using multiple class applications," IEEE Vehicular Technology Conference, Vol. 5, pp. 2835–2839, Fall 1999. [11] Xinjie Yang, Shahram Ghaheri-Niri, Rahim Tafazolli, "Enhanced Soft Handover Algorithms for UMTS System," IEEE Vehicular Technology Conference, Vol. 4, pp. 1539–1543, Fall 2000. [12] Xinjie Yang, Shahram Ghaheri-Niri, Rahim Tafazolli, "Evaluation of Soft Handover Algorithms for UMTS," 11th PIMRC 11th, Vol. 2, pp. 772–776, 2000. [13] X Yang, S Ghaheri-Niri, R Tafazolli, "Performance of power-triggered and Ec/Notriggered soft handover algorithms," 3G Mobile Communication Technologies, 26–28 March 2001. [14] ETSI, "Universal Mobile Telecommunication System (UMTS); Selection Procedures for the choise of radio transmission technologies of the UMTS," TR 101 112, V3.2.0, April 1998. [15] Deepak Bansal, Anurag Chandra, Rajeev Shorey, Ashutosh Kulushreshta and Ashish Verma, "Characterization of Mobility Patterns Based on Cellular Topography in a Cellular Radio System" in IEEE International Conference on Personal Wireless Communication (ICPWC 99), Jaipur, India, 1999. [16] T. Tugcu, C. Ersoy, "Application of a realistic mobility model to call admissions in ds-cdma cellular systems," IEEE Vehicular Technology Conference, Vol. 2, pp. 1047–1051, Spring 2001. [17] D. Lam, D.C. Cox, J. Widom, "Teletraffic modeling for personal communications services," IEEE Communications Magazine, Vol. 35, Iss. 2, pp. 79–87, Feb. 1997
