InCov: Indoor Coverage Prediction tool for UMTS, HSDPA, WLAN and future wireless data communications systems

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Research unit

INNOSUISSE

Project number

9155.1;3 PFES-ES

Project title

InCov: Indoor Coverage Prediction tool for UMTS, HSDPA, WLAN and future wireless data communications systems

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Short description (English)	InCov: Indoor Coverage Prediction tool for UMTS, HSDPA, WLAN and future wireless data communications systems
Abstract (English)	Current coverage prediction tools for indoor environements are not satisfying in terms of usefulness and accuracy. Precious time is waisted to gather the required building data and to compute inaccurate solutions which must be modified by experienced radio planner. An original approach, called PixelFlow, is proposed to develop a profitable and attractive planning tool for indoor radio network planning projects. The PixelFlow approach has a unique potential since it can provide sufficiently accurate results despite approximate building layouts and data. This will mitigate many disadvantages of current coverage prediction tools. The PixelFlow model from the EIA-FR, enhanced and validated in cooperation with Ericsson Switzerland would foster the competence of the project team. This project will also benefit from additional pratical experiences from experts of the Ericsson Indoor Competence Center in Spain. With such national and international references, the proposed prediction model could become a successful commercial product.

Category

Text

Short description
(English)

InCov: Indoor Coverage Prediction tool for UMTS, HSDPA, WLAN and future wireless data communications systems

Abstract
(English)

Current coverage prediction tools for indoor environements are not satisfying in terms of usefulness and accuracy. Precious time is waisted to gather the required building data and to compute inaccurate solutions which must be modified by experienced radio planner. An original approach, called PixelFlow, is proposed to develop a profitable and attractive planning tool for indoor radio network planning projects. The PixelFlow approach has a unique potential since it can provide sufficiently accurate results despite approximate building layouts and data. This will mitigate many disadvantages of current coverage prediction tools. The PixelFlow model from the EIA-FR, enhanced and validated in cooperation with Ericsson Switzerland would foster the competence of the project team. This project will also benefit from additional pratical experiences from experts of the Ericsson Indoor Competence Center in Spain. With such national and international references, the proposed prediction model could become a successful commercial product.