Combining Position Location Information and Network Performance Data for Simulating Location Aided Handover A. Markopoulos, P. Pissaris, S. Kyriazakos, Ch.Dimitriadis, Prof. E.D. Sykas National Technical University of Athens, Dept. of Electrical & Computer Engineering, Telecommunications Laboratory, 157 73 Athens, Greece Tel: +30 107721512, email:
[email protected]
ABSTRACT Position location of mobile terminals is one of the most challenging issues nowadays. Apart from the large number of value added services that can be offered to the users, network support is one of the most useful applications for the cellular operators. The information used for positioning can be evaluated locally, so that the operator can enhance the network’s performance without requiring user related information. In existing cellular systems one of the procedures that seems to cause serious network shortcomings is handover. This procedure is much more complicated in next generation systems (UMTS) and is of great interest for investigation and optimization. In this paper we have presented the structure of the LAH simulator. This simulator is a powerful platform to study cellular network procedures that are strongly related with the user location. This simulation platform will be used to prove the add-on of localization to the handover procedure. I. INTRODUCTION The Internet’s rapid growth has spurred development of new protocols and new algorithms to meet changing operation requirements- such as mobile networking, and quality-of-service support. Handover is one of the most critical procedures in cellular systems. Network operators give emphasis to optimize handover, since it is strongly related to dropped calls, network overload and subsequently users’ criticism. Handover can be seen as a blind procedure, if it is only based on the comparison of measurements, without the information of location. Since signal propagation and pathloss are complex in nature, we can expect unnecessary and wrong handover executions. In this paper we propose an architecture that combines user’s location with the measurements performed by the mobile terminal, to result in accurate positioning and execution of new intelligent handover algorithms. In the met Chapter 3 we present an architecture that combines location information with management reports to create a Mobile GIS, where the key performance indicators are placed geographically in the area of coverage. The main objective of this supporting
architecture is to provide the data for a set of tools that can optimize the network. Proposal for Location Aided Handover algorithms cannot be realistically tested, thus in Chapter 4 we present a simulator that is developed to evaluate alternative algorithms and validate their performance. This simulator will prove the value of Location Aided Handover, which is a set of algorithms that are triggered when the user is about to start signaling procedure for handover execution. In chapter 5, early results and trials are described, as well as future work that will be carried out. Finally, in chapter 6 we sum up with the conclusions. II. PROBING AND NETWORK SUPPORT BASED ON POSITION LOCATION The main idea is to evaluate Operation and Maintenance Center (OMC) measurements and combine them with user’s location to result in a mobile GIS (MGIS) with structured information about network performance. Above this MGIS, a set of tools is installed that have access to the GIS and control several mechanisms. The MGIS server will store information useful for the whole system. Some of the stored data will be handover failure, call setup success rate, field strength measurements and speech quality measurements. The MGIS data is used for radio- and neighbor-cell planning. By analyzing the MGIS data it is possible to detect areas where the handover success rate is low. At these critical areas a more complicated mechanism compared to the existing handover schemes should be applied. Since the mobile network operator can spatially adapt the network resources to the demand, the development and validation of a new intelligent handover algorithm (LAH) based on mobile phone location capability and on MGIS data can greatly improve QoS. These intelligent handover algorithms will be evaluated through a simulator (LAH Simulator), which not only includes improved validation of the behaviour of existing handover procedures but also gives a rich infrastructure for developing new algorithms. The opportunity to study algorithms in a controlled environment makes the comparison of results easier. [7,8]
III. SIMULATOR DEVELOPMENT A. Introduction While measurement and experimentation provide a means for exploring the “real world”, simulation is restricted to exploring a constructed, abstracted model of the world. Measurements are needed for a crucial “reality check”. Experiments are frequently vital for understudying the behavior of otherwise intractable systems. However, measurements and experiments have limitations in that can only be used to explore the existing handover procedures. They cannot be used to explore different possible new handover procedures. Simulations are not only complementary to analysis, but allow exploration of complicated scenarios that would be either difficult or impossible to analyze. Simulations can also play a vital role in helping researchers to develop intuition about the behavior of new handover procedures. So it is important to develop a reliable simulation environment for the investigation of the intelligent handover procedures. The development of the simulator that is implemented within the scope of CELLO project can be seen in two phases. The first phase is the development of a simulator, capable of investigating the typical handover procedure. The second phase will include the implementation of the LAH algorithm that will take into account the location of the user. Simulations that will be carried out during phase two should show the increased network performance that results from the enhanced handover algorithm.[9] B. Software Architecture The simulator must be easy to extend, by adding new functionality, and configurable so as to explore a range of scenarios and study new algorithms. The LAH simulator consists of several software modules. Each module is responsible for a set of tasks and communicates with other modules. One of the major objectives of the LAH simulator is the construction of a scalable system, where parameters, input data, models, algorithms, techniques and procedures can be easily integrated in the system. The goal is to provide adequate flexibility without constraining performance. The simulator is developed under Windows NT environment. Tasks such as low level-processing, traffic model implementation require high performance. So they are best served by expressing them in a compiled language such as C++. Furthermore, Visual C++ is used in order to have a friendly GUI. On this GUI the main ActiveX component is MapX, which provides us with the visualization of the area, we intent to simulate. Moreover, its main use is to demonstrate the movement of the users on the chosen geographical area. As an example of this, a map of the center of Athens (figure1) could be loaded in our simulator and the demonstration of users movement could be achieved by showing dots moving along the streets. Furthermore, it could be able to see not only by which BTS the user is being served but also the possible handover.
Figure 1: Athens Map loaded in MAPx Since the biggest part of the simulator will be implemented from scratch, the program will be initially generic and after testing each step, will become more and more precise getting closer to a real environment. For simulation environment, coverage maps based on real network and morphological data can be used later during the simulation. These data are being provided by a mobile network operator. The objective of our simulator is to show that using the positioning of each subscriber resulting improvement of handover procedure. It will be instructive to examine the most important components, which will combine the user location and the data that network provides in order to get the expected results. The general structure of the simulator is depicted in Figure 2. Input Inputparam parameters eters RX LEV distriblosses ution Prop agation
TrafficMm ovanagem eusersent module
U utionn User serdistrib distributio
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Measurem ent evaluation Interference module calculations
Figure 2: LAH simulator structure
The Location Server module (LSm) is the element that represents the LS entity in a real network. The LSm will get information about the actual user location and transform this to other coordinates that are possible outputs for LS. The LS entity can be characterized from a diagram that shows the location error as function of the probability. The diagrams that describe the LS entity performance for all kinds of areas (urban, suburban, open) are given as input to the simulator. The Measurement Evaluation module (MEm) communicates with the Mobility Management module (MMm) and Location Aided Handover module (LAHm), as its shows in figure 2, and is responsible for retrieving values from the maps for further process. MEm, retrieves the data from the stored maps in addition to the MGIS data for the area around the user. The MEm adds short-term fading to the RXLEVs and seeks this data and the rest of the MGIS data for the area in order to check if one of the ‘alarm’ thresholds is reached. In this case LAHm is informed. One of the most important issues that should be considered in the LAH simulator is the short-term fading. The simulator does not calculate the pathloss for each of the subscribers. Instead of that planning maps from the operator are used, so that the results are closer to the reality. Within the scope of the LAH simulator, the coverage maps will be exported from the planning tool of a cellular operator. An important parameter that the LAH simulations should take into account is the fading that results from the multipath propagation of the signals. Another vital issue supported by the simulator is the tracking of the user movement. The Mobility Management module (MMm) estimates the user location, taking into account the mobility models. The user location estimation is performed every 480ms. This happens in order to be synchronized with the time step at which the terminal sends and receives signals from the network. When we generate users we give them a specific direction and speed randomly. Then we estimate the new direction, having as main guide the assumption that users polarize in their initial direction by turning left or right in a few degrees. This amount is calculated using Gaussian distribution. [2] Traffic generation is one of the key challenges in modeling and simulating the cellular radio network. The traffic management module (TMm) is responsible to generate active users (users making a call and engaging free timeslots), put them in the environment of the simulator and check if some of the existing active users has to be removed. Active users are those users that are making a call and engage resources. In our case we assume that resources are a kind of free time slots (like in a TDM system). This is the source of traffic in the LAH simulator. These users are watched during the simulation. On the other hand, we are not concerned about users that are inactive since they cannot possibly initiate the handover procedure. Every time new active users are to be added in the simulator, it must be concerned where they will have to be placed on the map. Some areas are more possible to be selected for new active users first position.
It has to be mentioned that traffic generation is performed every 480msec of simulation time, for the reasons explained above. The TMm takes as input the total number of users, the user distribution…, the call duration and the call arrival rate. From these parameters we calculate the active users at every step. For a user to become active, that is to start a call, a time slot is needed to be free. So, the hole information of cells (Rx levels, No Of Users etc) is being loaded as a parameter and the No Of TCHs –this is included in performance data that have been provided-for the cell, in which the user is located, is being checked. If there are no free slots the call is blocked. The output of this module is the active users data i.e. every information about the users that the simulator is watching every 480 msec (e.g. active user’s call duration, position, etc.). This data could be used from the rest of the simulator and of course from the same module in the next step (next 480 msec).[1] Moreover, some performance measures are very important for the LAH Simulator. These are • Blocking Rate • Drop Call Rate (DCR) • Handover Success Rate (HOSR) Blocking Rate is the probability that the user cannot setup a call due to lack of resources. This important performance measure is being derived from the known number of TCHs, the number of users in each cell and the traffic that users create (Erlangs). Drop Call Rate shows the percentage of the calls that were dropped. In the LAH simulator we make the assumption that the DCR is inverse proportional to the RXLEV. Every time a user is trying to make a handover with the existing handover-procedure, the simulator checks to see if there will be a dropped call. It is obvious how we will derive the HOSR from this. Finally the component with the most fundamental role in the LAH simulator is the Location Aided Handover module (LAHm) that will enable the location-aided handover. This module will be the bedrock for the simulator. Its major goal is to implement an algorithm that proves the efficiency of the LAH. So, it will process the location-related information and apply one of the algorithms that will be implemented. Location-Aided Handover uses the information of instantaneous mobile location and the MGIS data to make the decision of the most appropriate target base station for handover. It is essential that the network is able to predict the target cell for handover well in advance in order to reserve the required resources. The module will initially decide about the handover scenario. These scenarios can be classified as follows: ping-pong, far-away, crossing borders, moving on the borders, critical area, etc. For each of the abovemention scenarios the appropriate technique will be selected. The system will be scalable, so that new techniques can be introduced in the system. [6]
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Figure 3: LAHm process The LAHm receives alarms from the MEm. If the alarm is triggered due to the fact that RXLEV has reached a threshold, then the module requests additional information from the MGIS for the area of user location. If additional data are needed, the LAHm sends another request to the MGIS. If the alarm was generated because the user was located in a critical area, the last step is skipped. Subsequently the handover scenario is recognized and the intelligent handover decision is taken. This procedure is be shown in figure 3. These requests are mainly questions about the area around the user. Useful information that can be retrieved is: what is the relation of the user’s movement and the cell-borders, is the target cell able to serve this user, etc. In addition it is possible that the LAHm requests network-information for the exact area where the user is moving, in case the alarm that was forward to LAHm was triggered by reaching an RXLEV threshold. To this end, LAH simulator exploits a split-module programming which cleanly separates the burden of simulator design, maintenance, extension and debugging from the simulator’s goal -the actual investigation of alternative LAH algorithms- by providing the simulation programmer with an easy-touse, reconfigurable, programmable simulation environment. [7,8,10] IV. EXPECTED RESULTS AND FUTURE WORK In the previous sections we have described the LAH simulator structure. In order to validate the system, a set of simulations will be performed. That is, we will estimate the network performance without the use of LAH module. At the next stage, the LAH module will be integrated in the simulator. We do foresee an increased stability and quality of the system with decreased signalling traffic and faster handover execution. Finally, handover failures, such as dropped
calls, “ping-pong” and “far-away-cell” effect will be minimized. The expected results of the simulations can be included in the improvement of network stability and efficiency. It has been found that a typical GSM network has an average handover failure of around 10% in city areas. Considering that users making calls while moving can require several handovers during a call, the call-drop probability increases. The causes of the handover failure, according to the network system are mainly, low field strength, quality or power budget. In the reality, some of the major reasons are the ones described in the previous sections. These causes cannot be easily detected from the network. Monitoring simultaneously the user’s movement in addition to the statistical data from MGIS, all these effects can be encountered. So, in the first step we expect the results such as DCR, Handover In Failure, Handover Success Rate, Blocking Rate to be keep up with the reality and the real data network provider knows. Then new intelligent algorithms (LAH algorithms) will be used in order to export useful conclusions about how could the operator improve the network sufficiency. The handovers will be executed only if needed. The selected target cells will be examined in terms of traffic load and geographical relevance with the user’s movement. Moreover, it is possible to use the mobile phone location capability to aid the actual handover algorithm. This is a clear advantage over the existing solutions, which are based only on the signal level observations. For example, the handover could be delayed if the mobile would be detected as moving along the border region of two cells. This way the "ping-pong effect" can be avoided. Also, if there are several neighbors to choose for target cell, the information of location may help to make the optimal choice. [10] V. CONCLUSIONS In this paper we have presented the structure of the LAH simulator. This simulator is a powerful platform to study cellular network procedures that are strongly related with the user location. This simulation platform will be used to prove the add-on of localization to the handover procedure. Meanwhile intelligent locationaided handover schemes are developed so that they can be integrated in the simulator. The simulator is developed both for GSM and UMTS. After the simulations will be carried out, LAH can be commercialized by proposing appropriate implementations for BSCs and other network elements. However, the first indications of our studies lead us to the conclusion that intelligent handover will minimize signaling overhead and system errors related to trivial handover decisions, increasing user satisfaction. VI. ACKNOWLEDGEMENT This work has been performed in the framework of the project IST CELLO, which is partly funded by the European Community. The Author(s) would like to acknowledge the contributions of their colleagues from VTT Information Technology, Cosmote Mobile
Telecommunications S. A., Center for PersonKommunikation, Elisa Communications Corporation, Motorola S.p.A, Institute of Communication and Computer Systems / National Technical University of Athens, Teleplan AS. VII. REFERENCES [1] B. Walke, “Mobile Radio Networks Networking and Protocols”, Wiley 1999 [2] M. E. Anagnostou and G. C. Manos, "Handover Related Performance of Mobile Communication Networks," Proc. Vehicular Tech. Conf. '94, Stockholm, Sweden, June 8p;10, 1994, pp. 111p;14. [3] G. P. Pollini, "A Catalog of Handover Algorithms for the Cellular Packet Switch," WINLAB Tech. Rep. TR-48, Rutgers, Jan. 1993. [4] G. L. Lyberopoulos, J. G. Markoulidakis, and M. E. Anagnostou, "The Impact of Evolutionary Cell Architectures on Handover in Future Mobile Telecommunication Systems," Proc. Vehicular Tech. Conf. '94, Stockholm, Sweden, June 8p;10, 1994, pp. 120p;24.
[5] J. Markoulidakis, J. Dermitzakis, G. Lyberopoulos, M. Theologou, “Handover Prioritised Schemes for Optimal Capacity and Overload Management in Cellular Mobile Systems”, VTC 1999 Fall, Amsterdam, The Netherlands [6] J. Lahteenmaki, S. Kyriazakos, P. Fournogerakis “ Using Mobile Location Techniques for Network Planning and Handover Optimization", 3G Infrastructures and Services 2001, July 2001, Athens, Greece [7] S. Kyriazakos, D.Drakoulis, G. Karetsos “Optimization of the Handover Algorithm based on the Position Location of the Mobile Terminals", IEEE-Benelux VTC, October 2000, Leuven, Belgium [8] S. Kyriazakos, D.Drakoulis, G. Karetsos “ Intelligent Handover Algorithm for Increasing Stability in Cellular Networks Based on the Position of the Mobile Terminals ", IST Mobile Summit, October 2000, Galway, Ireland [9] Sally Floyd,Vern Paxson "Difficulties in simulating the Internet" IEEE/ACm Transactions on Networking, Vol.9 No4. August 2001 [10] CELLO D8, LAH Requirement Specifications, September 2002
