Infinite-Dimensional Stochastic Analysis: White Noise Analysis and Generalized It CalculusWhile the subject of infinite-dimensional variational calculus, Wiener processes, stochastic differential equations (SDE) is vast, with many and varied subdisciplines, and with many and diverse applications, for our ...
Analysis, modeling, and simulation for better understanding of diverse complex natural and social phenomena often require powerful tools and analytical methods. Tractable approaches, however, can be developed with mathematics beyond the common toolbox. This book presents the white noise stochastic calculus...
This monograph presents a framework for infinite dimensional analysis based on white noise. This approach, which has many areas of application is both intuitive and efficient. Among the concepts and structures generalized to an infinite dimensional setting in this book are: spaces of test and general...
The first purpose of this paper is to introduce and study the counterpart of the white noise for the generalμ-Brownian motionW(μ). We also present a parallel, but different, setting, where the starting point is a set of signed measures associated with a positive definite kernel. Then, we...
White noise analysis is an advanced stochastic calculus that has developed extensively since three decades ago. It has two main characteristics. One is the notion of generalized white noise functionals, the introduction of which is oriented by the line of advanced analysis, and they have made much...
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Finally, the complex baseband models for digital modulators and detectors developed in previous chapter of this book, are incorporated to build a complete communication system model. If you would like to know more about the simulation and analysis of white noise, I urge you to read this a...
Book 2020, Optical Fiber Telecommunications VIIMarco Secondini Chapter An Introduction to Distributed Channel Coding 2.1.1 AWGN relay channel For the additive white Gaussian noise (AWGN) relay channel we can refine the model and characterize the input-output relation of the channel as YR=HSRXS+ZR...
Here {e,} is a Gaussian white noise process with mean zero and variance cr2 thatis independent of /x(0. Notice that/i, = £(M(0) + E(e.) = 0.Also,andyJt = £O<0 + £,)O<0 + e,-,-) = A2 for/ * 0.