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The knowledge of channel statistics, as a result of random fading, interference, and primary user activities, can be very helpful for a secondary user in making sound opportunistic spectrum access decisions in a cognitive radio network. It is therefore desirable to be able to efficiently and accurately estimate channel statistics, even for resource constrained secondary users like wireless sensors. In this paper the authors focus on the traditional ML (Maximum Likelihood) estimator. However, rather than using equal or uniform sampling/sensing intervals as is typically done, they introduce a random sampling/sensing based ML estimation strategy.
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