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A very important part of designing the UTRAN transmission network is the dimensioning of the required throughput for each Node B in the UMTS network. For this reason, the output of radio network planning is taken as input for the radio access network design. All the data can be divided into a number of parts, which represent clusters in RNC service areas. For each cluster, the number of base stations, their location and the maximum traffic demand (including all system procedures and services) have to be known. The calculation of maximum traffic demands shall consider not only traffic in the UMTS radio interface, but also control information indispensable for managing Node Bs. The transmission formats used in the interface between Node B and RNC in conjunction with served services are of great importance to the calculations to the Node B s throughput. It is mainly related to the transmission overhead, which varies from service to service. Detailed requirements for UTRAN transmission infrastructure and throughput dimensioning have been presented in Sections 17.1 17.3. The accurate analysis of maximum Node B throughput requirements, information about distance and propagation conditions in the radio path to each other Node B in the cluster are sufficient to design the UTRAN transmission infrastructure using Point-to-Point radio systems. Some types of radio lines used in backhaul networks and their parameters have been presented in Table 17.16. For the backhaul radio network solution, which makes use of the central station, the designer has to know the total capacity of all Node Bs in the cluster and the cluster size, which is defined as the maximum distance from between remote Node Bs in the cluster. To design the radio backhaul network, the operator has only to know the radio resources, which are being assigned to the operator by the

(5.114)

Simple approaches The most prominent, simple algorithms are geometry-based and Greedy methods For complex optimisation tasks in the context of UMTS radio network planning, these methods usually fail to come to competitive solutions However, they are typically easy to implement and can serve as useful tools during optimisation First, they can be used to generate reasonable starting solutions as an input to subsequent, more sophisticated methods Second, they can be used as a reference for sophisticated methods In addition to comparing the results of sophisticated methods to an arbitrary starting solution, it can be compared to the output of simple strategies This sheds a more realistic light on the value added by sophisticated optimisation Search Methods Search methods represent a compromise between running time and quality of results They rely on evaluating a multitude of alternative network configurations The level of detail of these evaluations directly scales their running time.

The structure of the schema follows the design of the instance questionnaire described a little earlier. Hopefully you will be able, having started at the element declaration for the <Questionnaire> element, to follow the logical flow of allowed and appropriate choices for the data that is to be collected. The definition of the SmokingHistoryGroup enables us to choose either a <Never-Smoked> element or a further choice group named EverSmokedGroup. The final member of the sequence within the EverSmokedGroup group is a Current-SmokerGroup group. In the schema as written, that group contains only a single element. The group could be extended further to explore questions about current smoking habits, however perhaps incorporating a choice of CurrentHeavy-SmokerGroup, CurrentModerateSmokerGroup, or CurrentLightSmokerGroup groups. The GenderRelatedGroup group enables us to include content appropriate to each gender. Just as with the smoking history questions, we could nest further choice or sequence groups. For example, if there was a family history of gynecological cancer as indicated by the content of the <GynCaFamilyHistory> element, then further questions could be added in another more deeply nested group. You should be able to see that <xsd:group> elements can be used to create complex nested structures that can, when sensibly used, reflect the logic of the information that it is appropriate to record (depending on the circumstances that apply). XSD Schema provides a further mechanism to enable us to choose which type of element to display in a particular setting. In this case, we are constrained to the inclusion of elements of the same datatype. Let s go on and examine substitution groups.