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Information about the radio coverage is essential for network planning, RF tuning, and Radio Resource Management (RRM) parameters optimization. Nowadays, network operators use prediction tools to produce this information. These tools are based on maps providing topographic and land use information (e.g. buildings, natural areas and roads), as well as on tuned propagation models. However, this approach is not fully accurate. Reasons for the inaccuracies are imperfections in the used geographic data, simplifications or approximations in the applied propagation models, and changes in the environment caused by, for example, constructions/demolitions or seasonal effects (foliage changes). Furthermore, variations in the traffic distribution and user profiles are a source for inaccurate results provided by prediction tools. For this reason, rigorous drive tests are performed in order to get more information about the real situation in the network and to gather measurement data for calibrating the used propagation models. Drive tests provide a picture of the end user perception in the field and enable the operator to identify locations causing poor performance and their corresponding cause (e.g. incorrect tilt or handover settings). Drive tests are, however, not ideal since they are expensive, time-consuming and cover only a limited (outdoor) part of the network due to access restrictions. Another disadvantage is that only a snapshot in time of the conditions in the field is captured. These difficulties with drive tests could be overcome if the User Equipments (UEs) in the network could be used for reporting the observed service quality along with the positions where the measurements are taken. The standardization of such UE reports is currently being carried out in the Third Generation Partnership Project (3GPP) [1]. These UE reports can be used to create a geographic map with overlay performance information, referred to as an X-map where X can stand for different types of performance
*The work presented in Appendix B was carried out within the FP7 SOCRATES project, which is partially funded by the Commission of the European Union. See http://www.fp7-socrates.eu/.
Self-Organizing Networks: Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE, First Edition. Edited by Juan Ramiro and Khalid Hamied.
© 2012 John Wiley & Sons, Ltd. Published 2012 by John Wiley & Sons, Ltd.
274 Appendix B: X-Map Estimation for LTE
information. A coverage map, for instance, would be a specific kind of X-map. The UE report processing is done by an X-map estimation function that continuously monitors the network, estimates its spatial performance characteristics, e.g. the coverage and throughput, and maps the UE measurements to an estimated geographic position. In Long Term Evolution (LTE), three different positioning techniques are foreseen, namely, the network-assisted version of Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) or Galileo, Observed Time Difference Of Arrival (OTDOA) and an enhanced Cell-ID positioning method [2]. In addition to the measurement data, the X-map estimation function may also use other sources of information, such as prediction data. The advantage of the X-map estimation function is that rigorous drive tests can be reduced. This will significantly reduce network maintenance costs for operators, ensure faster optimization cycles resulting in higher customer satisfaction and provide measurement data from areas that are not accessible for drive tests, e.g. narrow roads, forests, private land, houses or offices [1]. Apart from assisting the network operator in observing the network performance, the information embedded in an X-map may be used as an integral part of Self-Organizing Networks (SON) [3, 4], especially in functionalities addressing the optimization of coverage, capacity and quality.
The accuracy of an X-map depends on many factors such as the applied UE positioning technique, the UE measurement accuracy, the number of measurements taken and the network architecture. In particular, the positioning technique has a significant impact on the overall X-map estimation accuracy.
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