Christian Doerr
ABSTRACT
In order to find a configuration suitable to fulfill its needs, Cognitive Radios search the parameter configuration search space using one or more particular algorithms or heuristics. While each individual configuration tested uses a similar cost for evaluation (for example in airtime, computational cost for evaluation or power), many configurations will not yield any value to the radio and their exploration turns out to be a waste of resources. This paper introduces the application of fractional factorial designs to Cognitive Radios (CR), a technique to drastically prune the parameter search space while still yielding good results, thus enabling CRs to find the best possible configuration fast while using less resources for the search. We show that by using this technique CRs can evaluate a fraction of configurations while still correctly estimating the factors influencing its performance.
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Index Terms
- Optimizing for sparse training of Cognitive Radio networks
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