Abstract
One of the main concerns of telecommunications operators is related to network coverage. A weak coverage can lead to a performance decrease, not only in the user experience, when using the operators' services, such as multimedia streaming, but also in the overall Quality of Service. This paper presents a novel cloud-based framework of a semi-empirical propagation model that estimates the coverage in a precise way. The novelty of this model is that it is automatically calibrated by using drive test measurements, terrain morphology, buildings in the area, configurations of the network itself and key performance indicators, automatically extracted from the operator's network. Requirements and use cases are presented as motivations for this methodology. The results achieve an accuracy of about 5 dB, allowing operators to obtain accurate neighbour lists, optimise network planning and automate certain actions on the network by enabling the Self-Organising Network concept. The cloud implementation enables a fast and easy integration with other network management and monitoring tools, such as the Metric platform, optimising operators' resource usage recurring to elastic resources on-demand when needed. This implementation was integrated into the Metric platform, which is currently available to be used by several operators.
Highlights
Nowadays, there is an increasing demand of mobile users, which increases the use of telecommunication services, making network coverage estimation a concern for operators
The automation of the calibration process follows the Self-Organising Network (SON) paradigm, makes it possible to reduce the human effort, which results in a financial impact on the management of these networks
This paper presents a cloud-based framework of a novel semi-empirical propagation model that portrays, as accurately as possible, the propagation of an antenna
Summary
There is an increasing demand of mobile users, which increases the use of telecommunication services, making network coverage estimation a concern for operators. To simplify the planning process of a network, this paper presents a novel cloud-based framework of a semi-empirical propagation that using Drive Test (DT) measurements, terrain morphology, buildings information and configurations of the network itself to estimate the coverage in a precise way This optimised propagation model uses a cloud-based implementation, which allows its integration into a tool for. The main contribution of this paper is the creation of a novel generalised and new semi-empirical propagation model that can be applied simultaneously in micro and macro cell scenarios This propagation model introduces the innovation of being automatically calibrated with DTs as well as network KPIs, providing a realistic estimation of path loss each time data related to the antenna (tilt, azimuth, KPIs, DTs) is added or changed. By using this data in the proposed model, signal estimation over an entire cell area will become more precise and accurate
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