White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An alternative approach

Abstract: Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors.

Blind and semi-blind channel identification methods using second order statistics for OFDM systems

Abstract: Two new blind channel identification methods suited to multicarrier system (OFDM) exploiting the redundancy introduced by the adjunction of a cyclic prefix at the emitter and relying on the evaluation of the received signal autocorrelation matrix are presented. The proposed algorithms are able to identify any channel without any constraint on their zeroes location (including non-minimum phase channels) and are robust to the addition of white noise. Moreover a further enhancement of the estimation accuracy can easily be achieved by taking advantage of already present training symbols in current systems operating in a semi-blind context. Furthermore one of the two identification strategies has a very low arithmetical complexity which makes it particularly attractive in practice. Notice that these methods are not restricted to classical DFT-type modulators and still apply with any perfect reconstruction modulator.

Cumulants-based modulation classification technique in multipath fading channels

Abstract: Automatic modulation classification (AMC) is a classical topic in the signal classification field and is often performed when the modulation type is adaptive. For typical modulation types such as M-PSK and M-QAM, the fourth-order cumulant statistics are usually used for modulation classification. Besides, it is also known that the AMC performance can be seriously degraded by the effects of multipath fading in wireless channels when compared with the ideal white Gaussian noise channel. In this study, a new modulation classification method is proposed, exploring different features of normalised fourth-order cumulant statistics for a modified blind channel estimation method in the multipath fading channel. The relationship between the cumulants of the received signal and the multipath fading effects is established to cope with the channel impulse response in the new algorithm. Simulation results show that the performance of the proposed AMC method is much improved than that of previously proposed ones in terms of the probability of correct classification.