Jens Voigt, and Jörg Wagner*. Endowed Chair for Mobile Communications Systems and *Telecommunications Chair,. Dresden University of Technology.
OBJECT-ORIENTED MODELING OF A GENERIC MOBILE RADIO SYSTEM FOR DYNAMIC SYSTEM SIMULATION Jörg Fischer, Jürgen Deissner, Gerhard Fettweis, Dietrich Hunold, Ralf Lehnert*, Mathias Schweigel*, Jens Voigt, and Jörg Wagner* Endowed Chair for Mobile Communications Systems and *Telecommunications Chair, Dresden University of Technology D-01062 Dresden, Germany E-mail: {fischer,deissner,fettweis,hunold,lehnert,schweige,voigtje,wagner}@ifn.et.tu-dresden.de
KEYWORDS Discrete Event Simulation, System Dynamics, Telecommunications, Mobile Networks ABSTRACT The process of developing and enhancing a mobile radio system is a very ambitious task due to the inherent complexity and the manifold dynamics of such networks. Essentially, the problem leads to an analysis of the interferences between radio resources of limited number. Since the problem cannot be solved analytically, comprehensive simulation is a reasonable means to tackle it. Earlier, only selected aspects of mobile radio networks were investigated, neglecting other important features as, e.g., dynamics. Another way to investigate a network’s interference are measurements, which are, however, very expensive and only possible in existing networks. In this paper we introduce a method how a mobile radio system can be modeled for simulation, taking all important phenomena into account. The starting point is an objectoriented analysis that produces a generic class structure. All phenomena that are typical to the target under investigation, i.e. the versatile dynamics and the resources, are represented in this structure. Thus, enhancements of existing networks as well as future system concepts can be tested prior to their introduction. Results with the simulator show that the comprehensive modeling of relevant phenomena as the distinct aspects of dynamics is justified, thus providing much more accurate and expressive values than with a conventional static approach. 1 INTRODUCTION The rapidly increasing demand for a higher traffic load to be carried by mobile radio networks forces operators and system designers to enhance and optimize existing networks and
to develop entirely new mobile radio system solutions. To keep the development cost as low as possible, the potential increase in network capacity and/or quality due to the application of new features is to be estimated before their introduction into the network. However, due to the complexity and versatile inherent dynamics in the system, the design and optimization of mobile radio networks is a problem which can not be solved analytically. Known planning methodologies for mobile radio networks (Lambrecht 1995) are mostly based on static coverage predictions only. Networks which are already in operation can be optimized using measurements of quality parameters. Gathered experiences in connection with empirical methods can also be used by operators to optimize their networks. These planning and optimization methodologies, however, are bounded to single aspects of the network and do, in most cases, not take the network as a whole including its complexity and, especially, dynamics into account. In contrast, dynamic system simulation is able to examine all capacity- and quality-relevant dynamic aspects of a mobile radio system in their complexity. Therefore, this kind of simulation is the suitable way for the planning and optimization process regarding new features or entirely new mobile radio systems. For our approach of reusability, i.e. the WiNeS Wireless Network System Simulator (WiNeS 1999) as a common platform for the investigation of several mobile radio systems (Deissner 1999), the simulation model has to take the key properties of a generic mobile radio system into account. Therefore, in section 2 we describe our focus of modeling and simulation as a preparation for the description of our objectoriented analysis and the resulting key abstractions in a generic mobile radio system, which are presented in section 3. Special attention was given to the modeling of dynamics and resources, which is described in section 4. In section 5 we outline the WiNeS application and finally present some exemplary simulation results in section 6.
2 FOCUS OF MODELING AND SIMULATION
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2.1 Interference as a central issue
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Any network operator aims at the maximal exploitation of a limited set of radio channels to serve the demands of a growing number of mobile users at a high quality level. The systematic reuse of the radio channels and the lack of orthogonality in the implementation of multiple access schemes result in interference between radio connections which currently use the same radio channel (e.g., the same frequency). Clearly, interference is a central issue in the performance evaluation of mobile radio networks. It is closely related to the network’s capacity and quality (Fig. 1). Fully loaded re-
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Fig. 1. Relations between interferences, quality, and capacity
sources (a high network capacity) cause interference, which limits the network’s quality. So, in order to guarantee a certain quality level, a network operator must not exceed a certain capacity limit. It is the main goal of network planning to balance the relationship between capacity and quality. Network optimization mostly applies methodologies for interference reduction in order to further increase capacity and/or quality. Both tasks rely on an interference analysis and have to take the special properties of mobile radio networks, especially dynamics, into account. 2.2 Dynamics in Mobile Radio Networks Every mobile radio network exhibits dynamics in multiple ways (Fig. 2). First of all, dynamics is introduced to mobile radio networks by their most distinguishing characteristic: the user’s mobility. The users have different movement characteristics. The movement itself is the reason for dynamics in the network configuration, the radio propaga-
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Fig. 2. Dynamics in mobile radio networks
tion conditions, and the algorithms of the mobile radio system. The traffic load is heterogeneous and dynamic in two regards. On one hand, the traffic load can be irregularly distributed over the entire serving area in time and space, e.g. through changing accumulations of users. On the other hand, the traffic is and will, in future networks, more and more become a mix of services with various dynamic demands for resources (speech, e-mail, internet, video, file transfer, ...). In contrast to common wired access networks, the configuration of a mobile radio network is mutable. A mobile user might have to change his network access point during an active connection due to his movement or due to the network load. During this process the mobile user’s connection is handed off from one base station to another and, additionally, is maintained for a short time simultaneously via both base stations. Furthermore, the number of mobile users in the network as well as the state of these users (e.g., stand-by or on air) is continuously changing in accordance with the traffic load and the mobility of the users. Additionally, in a multihop system mobile devices can take on base station functionality, thus changing the network structure, too. Due to the user mobility, the conditions of the radio wave propagation are very dynamic as well. The mean signal power changes according to the distance between the transmitter and the receiver. Additional effects are shadowing and multipath propagation, which lead to signal fading, the Doppler effect and variant transmission delays. In current and, especially, future system solutions the air interface definition becomes more and more dynamic by itself, partly in reaction to the dynamic environment. Known features of a dynamic air interface are, e.g., frequency hopping, adaptive channel allocation, smart antennas, or the upcoming software radio (Mitola 1995; Fettweis 1997). Mobile radio networks can only be planned and optimized effectively, if all those dynamic characteristics are taken into account in their complexity. A selective usage of new features in mobile radio systems or new system solutions at all
can help to meet the requirements of the perpetually increasing capacity demands in those networks. System simulations of mobile radio networks in their entirety are an irreplaceable help for the estimation of the capacity gain in the network through the application of new features before they are actually installed as well as for maximizing the capacity gain by the selection of the optimal network configuration. 2.3 Simulations at System Level Due to the complexity of mobile radio networks it is not possible to include all of their properties in one single simulation model with a sensible effort. Instead, three levels of simulation can be identified: link level, system level, and protocol level simulations (Fig. 3).
inspection levels protocol system
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are independent of any implementation. Having mobile radio network planning and optimization in mind, the goal of such an analysis is to describe the complex dynamic behavior of a mobile radio system from a functional point of view. Since the modeling of an arbitrary network is required, the target of investigation in this case is a generic mobile radio system where the focus is on functional aspects. The result of the OOA is an abstract class structure that reflects the identified key abstractions (Fig. 4). The subsequent software implementation is based upon this structure. Since the emphasis of the modeling was on functionalities of a mobile radio system, elementary procedures, e.g. the resource allocation, can be found as classes in Fig. 4 [using UML notation (Rational et al. 1997)]. In every mobile radio network, network elements (normally base and mobile stations) communicate with each other over a common transmission channel. This basic principle just reveals the most important classes: network element and transmission channel. Most other classes are contained in them. Additionally, there are classes that describe the surroundings of the mobile radio network. These are the backbone network, that realizes the interconnection of the base stations, and the environment.
Fig. 3. Investigation focus of system simulations
3.1 Network Element
Link level simulations model in general one single physical link in detail with the goal to investigate at the efficiency of signal transmission and detection. The time resolution is hereby the duration of one bit or one chip. Simulations at system level, however, reproduce an entire mobile radio network (or parts of it) in software in order to evaluate the efficiency of the network and especially its resource management on the base of an interference analysis. System simulations have a coarser time resolution of e.g. the duration of a burst, a data packet, or a frame. Simulations at protocol level are focused on control sequences for services and mobility. Our inspection focus of the model of mobile radio networks described in this paper is clearly system level simulation, but with interfaces to both, protocol and link level.
As just identified, the network element is an important key abstraction in the generic mobile radio system that has relationships to many other classes. If the investigation focus is extended to the connection of network elements to the backbone network, the derived class network element with fixed access is used instead. Movement is an omnipresent and decisive feature of network elements in a mobile radio system. Since even no motion can be regarded as a certain kind of movement, this property is identified as a key abstraction of all network elements. The movement is connected via a “has-a” relationship to the network element. There is also a “uses-a” relationship between the movement and the environment, because the network element always moves within this environment. Every network element has a network access in order to characterize the access to the radio network. All running applications of a network element reside in the network access. So, the application related procedures [protocols in higher layers of the ISO OSI model (ISO 7498)] are associated with this class, too. But the protocols are not really a part of the class. Instead, the network access embodies an interface to the protocol level and represents the properties of the protocols at the system level. The main focus of the interference analysis is on the re-
3 OBJECT-ORIENTED ANALYSIS OF A GENERIC MOBILE RADIO SYSTEM According to the principles of object-oriented software design (Booch 1994), the first step in a complex programming project is an object-oriented analysis (OOA) of the target of investigation. In this procedure the target of investigation is searched for key abstractions (classes) that
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Fig. 4. Key abstractions of the functional structure of a generic mobile communications system
sources. They are utilized by running applications in the network access. For every resource that is assigned by the resource allocation a resource usage exists. Its task is to supervise the usage of a resource. It is assisted by one or more resource maintenances that provide maintenance algorithms (one in each instance) like e.g. power control and handoff. There is a hierarchy of several activation processes in a network element that produce dynamics in the network. At the top most level, the activation can instantiate a network element at arbitrary points in time. In an active network element two kinds of activities generate dynamics: the user activities and the network inherent activities. The user activities are connected by a “has-a” relationship to the network access because they initiate the service-specific requests for resources. These requests occur randomly according to the user’s service profile. The network inherent activities are, however, system induced, mostly periodic processes that run automatically in the network element. They can concern both the network element (relationship to the network access) and single resources (relationship to the resource usage). Eventually, within an active transmission the actual traffic load is produced by the data stream generator. Special attention was given the modeling of certain properties of the physical layer, which is necessary in order to evaluate the radio link quality, e.g. on the basis of bit error rates. For this reason, a network element has a modem and one or more antennas. These classes embody an interface to the link
level that represents the relevant link level properties of a modem or an antenna at system level. Via the relationship between the resource maintenance and the modem the parameters of the modem are adapted to the current propagation conditions, e.g. using power control. 3.2 Transmission Channel The transmission channel is used by the network elements for communication. Two classes are contained in it, the resource occupation and the radio wave propagation. The resource occupation registers the occupation of all resources available in the network. If a network element queries the transmission channel for the current resource occupation, the channel uses the radio wave propagation to determine the effective occupation (effect) at the network element’s position from the registered occupations (cause). 4 MODELING OF DYNAMICS AND RESOURCES Apart from the general class structure of a generic mobile radio network, special attention has been given the modeling of dynamics and resources. The importance of dynamics was pointed out in the introduction, since they have a strong influence on the network performance. A motivation for the latter
aspect is the central point of our simulation methodology: the interference analysis between radio resources. 4.1 Dynamics Dynamics in mobile radio networks are based on several reasons: the movement of the network elements and random activation processes as well as the resultant dynamic behavior of the radio propagation channel. The network elements observe the channel and respond with processes which adapt specific system characteristics (Fig. 5). All of these various aspects of dynamics can be modeled by the use of the objectoriented analysis from section 3. Reactions to Dynamics
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4.2 Resources As described in section 2.1, the limited availability of resources holds a pivot position in the performance evaluation of mobile radio networks at system level. Hence, in addition to the dynamics, the modeling of the resources has to be a further central issue. In the following, resources are classified at both the logical and the physical level.
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tion relationships between the network elements). As far as possible the system-specific algorithms are modeled in that class in which the appropriate system characteristics are modeled as well. For instance, a smart antenna algorithm can be found in the class antenna or the adaptation of modulation and data rate (data rate fall-back) in the class modem. Other algorithms, e.g. the change of the communication relationship (handoff, multihop-systems) or power control, are classified as resource maintenance algorithms.
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Fig. 5. Reasons for and reactions to dynamics in mobile radio networks
A basic characteristic of mobile radio networks is the movement of their network elements, which is to be modeled in the class movement. This class provides algorithms for the calculation of the current position dependent on, e.g., time, direction of movement, speed, and environment. Activation processes initiate activities on several levels: Network elements are instantiated by the activation. Upon the generation, important parameters are specified for a network element. While a network element is active the user activities determine the user-specific service profile. During the time of the service usage the data stream generator generates activities on the burst or bit level. The activation processes as well as the user movement influence the radio wave propagation. The physical effects of the electromagnetic wave propagation are modeled in the radio wave propagation, a part of the transmission channel. Due to the movement of the network elements and the variable number of currently transmitting network elements the propagation and reception conditions change permanently. The mobile radio network has to react on the dynamics. It has system-specific algorithms for the adaptation of certain system characteristics to the changed channel conditions. These system characteristics are for instance: • smart antennas (modeled in the class antenna), • smart modems (modeled in the class modem) or • the network hierarchy (modeling due to the communica-
4.2.1 Logical Level All network elements share the same transmission channel, which - on the logical level - provides an overall channel capacity for all information transfers between the network elements, Fig. 6. Multiple access techniques (frequency (FDMA), time (TDMA), code (CDMA), and space division multiple access (SDMA)) unambiguously subdivide that overall channel capacity into logical resources according to parameters like frequency slot, time slot, code, spatial element, and any combination of them. Each network element can be assigned one or more different assignable resources. The network element internally handles resources by means of their logical parameters, e.g. the frequency slot and the code in case of the UTRA/FDD, a FDMA/CDMA system. However, in the relationship between the network element and the transmission channel another resource definition is required that reflects the physical level. Channel Capacity Multiple Access Technique logical resources (defined by the parameters: frequency, time, code, space)
Fig. 6. Logical resource parameters
4.2.2 Physical Level On the physical level, the information is carried through
vide a querying network element with the occupation that is effective for it at its point in time and space. 5 APPLICATIONS The object-oriented class structure of a generic mobile radio system has been implemented in a WiNeS Wireless Network System Simulator (WiNeS 1999). This simulator uses the discrete-event (DE) domain of the public domain software Ptolemy by UC Berkeley (Lee 1990) as simulation machine. For the simulation of mobile radio networks, this DE domain was enhanced in order to handle mutable system configurations (Voigt 1998). The object-oriented modeling of a generic mobile radio system enables the implementation of different applications on a common basis. The three different WiNeS applications which have already been implemented aim at the investigation of GSM radio networks, of a 60 GHz wireless broadband communications system (Aue 1996) as well as of a 5 GHz Integrated Broadband Mobile System [IBMS] (Bronzel 1998). All three implementations are derivatives of common abstract base classes, which reflect the class structure found in the OOA and define interfaces. Fig. 8. shows the internal structure of the WiNeS simulator. The abstract base classes form the WiNeS kernel and the system modules realize the functionality of a specific mobile radio system. For all three applications the DE domain is used as the simulation machine. System Module System Module
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the transmission channel in the physical signal properties, i.e. in the power occupations of the physical resource parameters: frequency, time, and space, Fig. 7. If two resources differ in one physical parameter and the mutual interference between these resources can be neglected solely due to that different physical parameter, we denote the two resources as separable in relation to that parameter. For example, in an UTRA/FDD system model, the frequency may be considered as a separable physical resource parameter. Space is in principle a non-separable physical resource parameter, because all cellular networks apply a spatial reuse scheme, which leads to a multiple assignment (and occupation) of the same resources in different places (refer to section 2.1). The mutual interference, which is caused by the spatial reuse, is a key objective of the performance evaluation of cellular networks and can therefore only be excluded for very long distances. Upon transmission, network elements register occupations by means of their separable physical resource parameters in the transmission channel, which stores them within its resource occupation. The separable physical parameters as order and search criteria thereby reduce the effort for updating the occupations and make the search for occupations of resources most efficient. Since the causes of occupations, the network elements, are registered, the resource occupation always represents the status, which physical resource parameter is occupied. However, to what degree, i.e. with how much signal power, the cause of an occupation currently occupies the respective resource parameter, is variant (e.g. by power control and the current position of the transmitter) and is therefore not stored within the resource occupation. They are queried in the occupying network element instead. Upon reception, network elements query the transmission channel for occupations of resource parameters at a certain point in time and space. The transmission channel collects all occupation information of relevant transmitting network elements. Then it propagates the signals by use of the propagation in order to pro-
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Fig. 9 is a snapshot of the Ptolemy block editor for the GSM radio network simulator implementation. It shows the modular and hierarchical structure of the model. On the right hand side a model of the mobile radio network can be seen on its highest abstraction level: base transceiver stations and mobile stations as network elements as well as a transmission channel. The left hand side shows the model of one mobile station: Icons for user activities, inherent activities, and a network element core. The latter block includes all resource-specific classes
Fig. 9. Simulation structure of the GSM radio network model
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(network access, resource occupation) as well as the antenna and the modem. Another system module for the third generation mobile radio system UMTS is under development and will be finished by the end of the year 1999.
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6 RESULTS
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The different WiNeS applications have been used to solve engineering and mobile radio network design problems, e.g. (Voigt 1999; Hunold 1998). As results show, the comprehensive modeling of relevant and important phenomena in mobile radio networks, as the versatile aspects of dynamics, provides much more accurate values of interesting network parameters (e.g. C/I, outage probability, blocking rate etc.). As an example, the GSM simulator implementation* has been used to investigate the impact of smart antennas (as an additional feature of dynamics) on the network performance. Fig. 10 shows some results in a GSM test area which consists of seven base stations. One of these base stations is sectorized and equipped with smart antennas which allow spatial filtering by interference reduction (SFIR) using beam switching. The results with 200 Erlang traffic load indicate that smart antennas increase the mean C/I ratio remarkably whereas the distribution of the C/I is ‘smoothed’. Furthermore, the interference situation in a 60 GHz campus and indoor TDMA system (Aue 1996) was investigated by dynamic simulations using the 60 GHz WiNeS application. A generic office-like environment was used in order to make common statements for such environments. Therefore, different room classes in typical office buildings were identified and various antenna types for these room classes were
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proposed. With this system setup it was shown that the assumed traffic time slots can be viewed to be noise-limited in single rooms and hallways with a probability of about 95%. Large open areas have a probability of up to 10% to be interference-limited (Voigt 1999). 7 CONCLUSIONS Mobile network designers, planners, and optimizers are faced with the comprehensive complexity of mobile radio systems and their inherent versatile dynamics. Thus, an exhaustive investigation of such systems inevitably requires system simulation as a means of analysis. Essentially, the network planning and optimization task leads to a contemplation of the interference between radio resources. Due to this basic simulation task, a meaningful resource definition is necessary. The separation of logical and
physical resources fulfills this requirement. In this way, resources can be managed and assigned at the logical level, and analyzed according to potential interference at the physical level. Since an increasing degree of flexibility is introduced in the definition of air interfaces, new network approaches will come up in the future more frequently. It is important to support this flexibility in the structure of the network simulator. A way to realize this is our generic model of a mobile radio system. It enables the implementation of arbitrary networks. Three examples with very different air interfaces and structures have been presented, a fourth is under development. Thus, the generic approach is open for new, currently unforeseen mobile radio systems. It was pointed out that mobile radio networks are characterized by a high complexity and manifold dynamics. In order to break down the complexity and make it manageable, an object-oriented software development approach was chosen. Additionally, a way to handle the complexity is the implementation of interfaces to both the link and the protocol level that lie out of the initial scope of investigation. Many dynamic phenomena in mobile radio networks are transient procedures as, e.g., adaptive antenna algorithms, data rate fall-back, or adaptive channel allocation. Their behavior has a non-negligible influence on the system performance. Also, movement of network elements and, consequently, perpetually changing radio propagation conditions have their impact on the system. Thus, it is indispensable to involve all these phenomena in the process of modeling. It was shown in the paper how the dynamic features are represented in the object-oriented class structure. In future developments, WiNeS will be enhanced towards an embracing and powerful tool for dynamic planning and optimization of mobile radio networks. Investigations will result in huge amounts of data to evaluate. Only an effective management of these data combined with a meaningful visualization and animation of important simulation aspects will together result in a convenient development environment for mobile radio networks. 8 REFERENCES
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