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The cognitive radio paradigm is based on the ability to detect the presence of primary users in a given frequency band. In this scenario a spectrum monitor may estimate the signal power levels of all frequency channels in the band of interest, together with the background noise level. We address Maximum Likelihood estimation for this problem, exploiting a priori knowledge about the primary network, summarized in the spectral shape of primary transmissions. An iterative asymptotic ML estimate is proposed, which can be further simplified in order to obtain a computationally more efficient Least Squares estimator with performance very close to the Cramer-Rao lower bound in several cases of interest.