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This book presents essential perspectives on digital convolutions
in wireless communications systems and illustrates their
corresponding efficient real-time field-programmable gate array
(FPGA) implementations. FPGAs or generic all programmable devices
will soon become widespread, serving as the "brains" of all types
of real-time smart signal processing systems, like smart networks,
smart homes and smart cities. The book examines digital convolution
by bringing together the following main elements: the fundamental
theory behind the mathematical formulae together with corresponding
physical phenomena; virtualized algorithm simulation together with
benchmark real-time FPGA implementations; and detailed,
state-of-the-art case studies on wireless applications, including
popular linear convolution in digital front ends (DFEs); nonlinear
convolution in digital pre-distortion (DPD) enabled high-efficiency
wireless RF transceivers; and fast linear convolution in massive
multiple-input multiple-output (MIMO) systems. After reading this
book, students and professionals will be able to: * Understand
digital convolution with inside-out information: discover what
convolution is, why it is important and how it works. * Enhance
their FPGA design skills, i.e., enhance their FPGA-related
prototyping capability with model-based hands-on examples. *
Rapidly expand their digital signal processing (DSP) blocks: to
examine how to rapidly and efficiently create (DSP) functional
blocks on a programmable FPGA chip as a reusable intellectual
property (IP) core. * Upgrade their expertise as both "thinkers"
and "doers": minimize/close the gap between mathematical equations
and FPGA implementations for existing and emerging wireless
applications.
This book presents essential perspectives on digital convolutions
in wireless communications systems and illustrates their
corresponding efficient real-time field-programmable gate array
(FPGA) implementations. FPGAs or generic all programmable devices
will soon become widespread, serving as the "brains" of all types
of real-time smart signal processing systems, like smart networks,
smart homes and smart cities. The book examines digital convolution
by bringing together the following main elements: the fundamental
theory behind the mathematical formulae together with corresponding
physical phenomena; virtualized algorithm simulation together with
benchmark real-time FPGA implementations; and detailed,
state-of-the-art case studies on wireless applications, including
popular linear convolution in digital front ends (DFEs); nonlinear
convolution in digital pre-distortion (DPD) enabled high-efficiency
wireless RF transceivers; and fast linear convolution in massive
multiple-input multiple-output (MIMO) systems. After reading this
book, students and professionals will be able to: * Understand
digital convolution with inside-out information: discover what
convolution is, why it is important and how it works. * Enhance
their FPGA design skills, i.e., enhance their FPGA-related
prototyping capability with model-based hands-on examples. *
Rapidly expand their digital signal processing (DSP) blocks: to
examine how to rapidly and efficiently create (DSP) functional
blocks on a programmable FPGA chip as a reusable intellectual
property (IP) core. * Upgrade their expertise as both "thinkers"
and "doers": minimize/close the gap between mathematical equations
and FPGA implementations for existing and emerging wireless
applications.
This volume is a collection of papers presented at the
international conference on Nonlinear Mathematics for Uncertainty
and Its Applications (NLMUA2011), held at Beijing University of
Technology during the week of September 7--9, 2011. The conference
brought together leading researchers and practitioners involved
with all aspects of nonlinear mathematics for uncertainty and its
applications. Over the last fifty years there have been many
attempts in extending the theory of classical probability and
statistical models to the generalized one which can cope with
problems of inference and decision making when the model-related
information is scarce, vague, ambiguous, or incomplete. Such
attempts include the study of nonadditive measures and their
integrals, imprecise probabilities and random sets, and their
applications in information sciences, economics, finance,
insurance, engineering, and social sciences. The book presents
topics including nonadditive measures and nonlinear integrals,
Choquet, Sugeno and other types of integrals, possibility theory,
Dempster-Shafer theory, random sets, fuzzy random sets and related
statistics, set-valued and fuzzy stochastic processes, imprecise
probability theory and related statistical models, fuzzy
mathematics, nonlinear functional analysis, information theory,
mathematical finance and risk managements, decision making under
various types of uncertainty, and others.
This volume is a collection of papers presented at the
international conference on Nonlinear Mathematics for Uncertainty
and Its Applications (NLMUA2011), held at Beijing University of
Technology during the week of September 7--9, 2011. The conference
brought together leading researchers and practitioners involved
with all aspects of nonlinear mathematics for uncertainty and its
applications. Over the last fifty years there have been many
attempts in extending the theory of classical probability and
statistical models to the generalized one which can cope with
problems of inference and decision making when the model-related
information is scarce, vague, ambiguous, or incomplete. Such
attempts include the study of nonadditive measures and their
integrals, imprecise probabilities and random sets, and their
applications in information sciences, economics, finance,
insurance, engineering, and social sciences. The book presents
topics including nonadditive measures and nonlinear integrals,
Choquet, Sugeno and other types of integrals, possibility theory,
Dempster-Shafer theory, random sets, fuzzy random sets and related
statistics, set-valued and fuzzy stochastic processes, imprecise
probability theory and related statistical models, fuzzy
mathematics, nonlinear functional analysis, information theory,
mathematical finance and risk managements, decision making under
various types of uncertainty, and others.
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