MbP™ (Measurement-based Prediction)

MbP (Measurement-based Prediction) is a unique, patented process which provides accurate assessment of network coverage at specific sites. It achieves this by combining data from traditional network planning tools with data from real-world local measurements to provide a comprehensive and detailed report which takes into account specific features of the site including terrain, buildings and trees. MbP is typically used to assist in the prediction and measurement of a range of network optimisation parameters including frequency planning, neighbour lists, interference and the removal of unwanted sites.

Click here to download the MbP White Paper (PDF):
Technical White Paper: Measurement-based Prediction

MbP approach

Typically, MbP achieves a standard deviation of the prediction error of between 5 dB and 7 dB. For example, MbP achieves a prediction error of 5.7 dB when compared with the standard empirical model which produces a standard deviation of prediction error of 8.6 dB. MbP also achieves hit rates in excess of 90%, compared with other techniques which traditionally achieve 82%.

MbP combines the relative simplicity of the semi-empirical methods commonly used in commercial network planning tools with the added assurance of local measurements by encapsulating valuable measurement information in the prediction result. This combination allows a more accurate prediction of the path loss in and around an experimental site before its deployment and provides network planners with higher confidence in the performance of a particular site or group of sites. The added accuracy also has a direct positive impact during the optimisation stages of the network, realised in terms of better coverage, capacity and quality for a given number of sites.

MbP is available from Andrew as an option within the Odyssey™ prediction tool.

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Benefits

• Flexible reporting which allows for accurate prediction on a limitless number of antenna configurations
• Accurate prediction of path loss in and around an experimental site prior to deployment to improve final performance and reduce risk
• Improved accuracy to improve 2G and 3G network optimisation resulting in better coverage, capacity and quality across all sites
• Seamless integration with other planning tools

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