Non-Data-Aided Symbol RateEstimation of Linearly Modulated Signals

Abstract

The estimation of the symbol rate of a linearly modulated signal is addressed, with special focus on low signal-to-noise ratio (SNR) scenarios. This problem finds application in automatic modulation classification and signal monitoring. A maximum-likelihood (ML) approach is adopted to derive practical estimators, exploiting information on the cyclostationarity of the modulated signal as well as knowledge of the received signaling pulse shape. The structure of the ML estimator suggests a two-step estimation procedure, whereby an initial coarse search determines first a neighborhood from which a subsequent fine search yields the final symbol rate estimate. Links between the ML approach and previous results from the literature in symbol rate estimation are established as well. The proposed scheme is applicable even for small excess bandwidths, at the cost of a higher complexity with respect to simpler estimators known to fail under such conditions.