Stochastic approximation with long range dependent and heavy tailed noise
Stability and convergence properties of stochastic approximation algorithms are analyzed when the noise includes a long range dependent component (modeled by a fractional Brownian motion) and a heavy tailed component (modeled by a symmetric stable process), in addition to the usual martingale noise. This is motivated by the emergent applications in communications. The proofs are based on comparing suitably interpolated iterates with a limiting ordinary differential equation. Related issues such as asynchronous implementations, Markov noise, etc. are briefly discussed.
stochastic approximation long range dependence heavy tailed noise o.d.e. limit convergence in ?th mean 60h30 62l20 93e35