ML Estimation of Signal Amplitude in Laplace Noise

Estimation of the received signal amplitude is critical to the optimization of the performance of detectors for data communication systems operating in the presence of impulse-like interference. A binary data communication system in the presence of Laplace noise is considered. Maximum likelihood estimators are derived for both data-aided and non- data-aided cases. A ML-based non-data-aided absolute median estimator with simpler implementation is also proposed. The performances of the proposed estimators are examined and compared to the well-known mean estimator and absolute moment estimator in terms of their mean- squared errors. Numerical results quantify the superiority of the optimal ML estimators over the conventional estimators.