Performance of DPSK Signals with Quadratic Phase Noise

Nonlinear phase noise induced by the interaction of fiber Kerr effect and amplifier noises is a quadratic function of the electric field. When the dependence between the additive Gaussian noise and the quadratic phase noise is taking into account, the joint statistics of quadratic phase noise and additive Gaussian noise is derived analytically. When the error probability for differential phase-shift keying (DPSK) signals is evaluated, depending on the number of fiber spans, the signal-to-noise ratio (SNR) penalty is increased by up to 0.23 dB due to the dependence between the Gaussian noise and the quadratic phase noise.Comment: 15 pages, 2 figure

Mapping multiplicative to additive noise

The Langevin formulation of a number of well-known stochastic processes involves multiplicative noise. In this work we present a systematic mapping of a process with multiplicative noise to a related process with additive noise, which may often be easier to analyse. The mapping is easily understood in the example of the branching process. In a second example we study the random neighbour (or infinite range) contact process which is mapped to an Ornstein-Uhlenbeck process with absorbing wall. The present work might shed some light on absorbing state phase transitions in general, such as the role of conditional expectation values and finite size scaling, and elucidate the meaning of the noise amplitude. While we focus on the physical interpretation of the mapping, we also provide a mathematical derivation.Comment: 22 pages, 4 figures, IOP styl

Multiscale Analysis for SPDEs with Quadratic Nonlinearities

In this article we derive rigorously amplitude equations for stochastic PDEs with quadratic nonlinearities, under the assumption that the noise acts only on the stable modes and for an appropriate scaling between the distance from bifurcation and the strength of the noise. We show that, due to the presence of two distinct timescales in our system, the noise (which acts only on the fast modes) gets transmitted to the slow modes and, as a result, the amplitude equation contains both additive and multiplicative noise. As an application we study the case of the one dimensional Burgers equation forced by additive noise in the orthogonal subspace to its dominant modes. The theory developed in the present article thus allows to explain theoretically some recent numerical observations from [Rob03]

Phase Ordering in Chaotic Map Lattices with Additive Noise

We present some result about phase separation in coupled map lattices with additive noise. We show that additive noise acts as an ordering agent in this class of systems. In particular, in the weak coupling region, a suitable quantity of noise leads to complete ordering. Extrapolating our results at small coupling, we deduce that this phenomenon could take place also in the limit of zero coupling.Comment: 8 pages, 7 figure

Stochastic resonance in bistable systems: The effect of simultaneous additive and multiplicative correlated noises

We analyze the effect of the simultaneous presence of correlated additive and multiplicative noises on the stochastic resonance response of a modulated bistable system. We find that when the correlation parameter is also modulated, the system's response, measured through the output signal-to-noise ratio, becomes largely independent of the additive noise intensity.Comment: RevTex, 10 pgs, 3 figure

Discriminating dynamical from additive noise in the Van der Pol oscillator

We address the distinction between dynamical and additive noise in time series analysis by making a joint evaluation of both the statistical continuity of the series and the statistical differentiability of the reconstructed measure. Low levels of the latter and high levels of the former indicate the presence of dynamical noise only, while low values of the two are observed as soon as additive noise contaminates the signal. The method is presented through the example of the Van der Pol oscillator, but is expected to be of general validity for continuous-time systems.Comment: 12 pages (Elsevier LaTeX class), 4 EPS figures, submitted to Physica D (4 july 2001

Consistency of Causal Inference under the Additive Noise Model

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting