Stochastic process

About: Stochastic process is a research topic. Over the lifetime, 31227 publications have been published within this topic receiving 898736 citations. The topic is also known as: random process & stochastic processes.

Langevin equations for continuous time levy flights

Abstract: We consider the combined effects of a power law L\'evy step distribution characterized by the step index f and a power law waiting time distribution characterized by the time index g on the long time behavior of a random walker. The main point of our analysis is a formulation in terms of coupled Langevin equations which allows in a natural way for the inclusion of external force fields. In the anomalous case for f2 and g1 the dynamic exponent z locks onto the ratio f/g. Drawing on recent results on L\'evy flights in the presence of a random force field we also find that this result is independent of the presence of weak quenched disorder. For d below the critical dimension ${\mathit{d}}_{\mathit{c}}$=2f-2 the disorder is relevant, corresponding to a nontrivial fixed point for the force correlation function.

A Markovian approach for modeling packet traffic with long-range dependence

Abstract: We present a simple Markovian framework for modeling packet traffic with variability over several time scales. We present a fitting procedure for matching second-order properties of counts to that of a second-order self-similar process. Our models essentially consist of superpositions of two-state Markov modulated Poisson processes (MMPPs). We illustrate that a superposition of four two-state MMPPs suffices to model second-order self-similar behavior over several time scales. Our modeling approach allows us to fit to additional descriptors while maintaining the second-order behavior of the counting process. We use this to match interarrival time correlations.

Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm

Abstract: Convergence analysis of stochastic gradient adaptive filters using the sign algorithm is presented in this paper. The methods of analysis currently available in literature assume that the input signals to the filter are white. This restriction is removed for Gaussian signals in our analysis. Expressions for the second moment of the coefficient vector and the steady-state error power are also derived. Simulation results are presented, and the theoretical and empirical curves show a very good match.

Stochastic power control for cellular radio systems

Abstract: For wireless communication systems, iterative power control algorithms have been proposed to minimize the transmitter power while maintaining reliable communication between mobiles and base stations. To derive deterministic convergence results, these algorithms require perfect measurements of one or more of the following parameters: (1) the mobile's signal-to-interference ratio (SIR) at the receiver; (2) the interference experienced by the mobile; and (3) the bit-error rate. However, these quantities are often difficult to measure and deterministic convergence results neglect the effect of stochastic measurements. We develop distributed iterative power control algorithms that use readily available measurements. Two classes of power control algorithms are proposed. Since the measurements are random, the proposed algorithms evolve stochastically and we define the convergence in terms of the mean-squared error (MSE) of the power vector from the optimal power vector that is the solution of a feasible deterministic power control problem. For the first class of power control algorithms using fixed step size sequences, we obtain finite lower and upper bounds for the MSE by appropriate selection of the step size. We also show that these bounds go to zero, implying convergence in the MSE sense, as the step size goes to zero. For the second class of power control algorithms, which are based on the stochastic approximations method and use time-varying step size sequences, we prove that the MSE goes to zero. Both classes of algorithms are distributed in the sense that each user needs only to know its own channel gain to its assigned base station and its own matched filter output at its assigned base station to update its power.

Weak Ergodicity Breaking in the Continuous-Time Random Walk

Abstract: The continuous-time random walk (CTRW) model exhibits a nonergodic phase when the average waiting time diverges. Using an analytical approach for the nonbiased and the uniformly biased CTRWs, and numerical simulations for the CTRW in a potential field, we obtain the nonergodic properties of the random walk which show strong deviations from Boltzmann-Gibbs theory. We derive the distribution function of occupation times in a bounded region of space which, in the ergodic phase recovers the Boltzmann-Gibbs theory, while in the nonergodic phase yields a generalized nonergodic statistical law. DOI: 10.1103/PhysRevLett.94.240602