Non-Data-Aided Estimation of Signal Level in Unknown Noise Using Empirical Characteristic Function

In this paper, we propose a new approach for signal level estimation in binary phase shift keying (BPSK) modulation based on the empirical characteristic function (ECF), when the probability density function (PDF) of the noise is unknown. Then, we compare our proposed method with two other estimators that are suggested for systems with known noises. Numerical results show that, in the presence of Laplace noise, the ECF estimator has a better performance in low signal-to-noise ratios (SNR) in comparison with previously proposed methods. Moreover, the proposed method does not require the knowledge of noise PDF and works without any training sequence.