Community Research and Development Information Service - CORDIS

Practicality of the multi-standard selection algorithms

An important issue to address is the practicality of the proposed multi-standard selection algorithms. A subset of the algorithms use knowledge of the location of a user relative to the network coverage regions and/or general statistical information about upcoming network coverage to make a more informed decision in the standard selection process. This information needs to be made available to the user terminal without any modifications to the network entities or standards, and so the feasibility with which it can be obtained has been determined. The ability of the algorithms to select the most suitable standard depends on the accuracy of the information available in the selection process. It is therefore important to examine the impact of inaccurate information in this process; to determine the effectiveness of the algorithms with realistic estimates of the required information as would be experienced in a practical implementation.

There are several standardised techniques for determining the location of a mobile phone including cell ID, network assisted GPS, and time difference of arrival, and this information can be made available to third party applications via the Open Service Access (OSA) Application Programming Interface (API). Obtaining accurate information about forthcoming network coverage is difficult, as it is hard to predict and dependent on a multitude of factors including the characteristics of the immediate surroundings and mobile objects. Coverage maps can be used to provide broad estimates of upcoming coverage. Alternatively, trial rail journeys can be carried out to determine coverage availability or this information could be gathered from the user terminals as people travel around. One of the algorithms only requires general statistical information regarding network coverage periods, which could be embedded in the user terminal software. Detailed performance evaluation has shown that the proposed algorithms remain effective in the presence of highly inaccurate information regarding the location of the user relative to the network coverage regions.

Reported by

University of York
Heslington -
YO1 5DD York
United Kingdom
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