Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications

Adaptive coded and modulation (ACM) is an effective measure to resist rain attenuation in Ka-band satellite communications. The accuracy of the estimator for signal-to-noise ratio (SNR) is one of the main factors that affect ACM performance. This paper establishes the channel model of satellite communication in Ka-band and derives and analyzes data-aided (DA) maximum likelihood (ML) and method-of-moments estimators for SNR of continuous phase modulation (CPM) in Ka-band fading channel. Simulations and analysis indicate that the normalized mean square error (NMSE) of the DA ML estimator is closer to the Cramer-Rao bounds (CRB) at low SNR, but the performance worsens with the SNR increases from medium to high values. The M2M4 estimator performs poorly at low SNR and best at medium SNR. When the SNR is low, the performance of the DA ML estimator is better than the M2M4 estimator; however, the two estimators have similar performance when SNR is high. The performances of the two estimators will become increasingly better the greater the lengths of the observation signals become. However, the influence of the signal length will become increasingly smaller as the SNR becomes larger.