Without conceding a blemish in the first edition, I think I had best come clean
and admit that I embarked on a second edition largely to adopt a more geometric
approach to the detection of signals in white Gaussian noise. Equally rigorous, yet
more intuitive, this approach is not only student-friendly, but also extends more
easily to the detection problem with random parameters and to the radar problem
Modeling and simulation of nonlinear systems provide communication system designers
with a tool to predict and verify overall system performance under nonlinearity and
complex communication signals. Traditionally, RF system designers use deterministic
signals (discrete tones), which can be implemented in circuit simulators, to predict the
performance of their nonlinear circuits/systems. However, RF system designers are usually
faced with the problem of predicting system performance when the input to the system
is real-world communication signals which have a random nature.
Before delving into the details of orthogonal frequency division multiplexing (OFDM), relevant
background material must be presented first. The purpose of this chapter is to provide the necessary
building blocks for the development of OFDM principles. Included in this chapter are reviews of stochastic
and random process, discrete-time signals and systems, and the Discrete Fourier Transform (DFT). Tooled
with the necessary mathematical foundation, we proceed with an overview of digital communication
systems and OFDM communication systems. We conclude the chapter with summaries of the OFDM
wireless LAN standards currently in existence and a high-level comparison of single carrier systems versus
OFDM.
Part I provides a compact survey on classical stochastic geometry models. The basic models defined
in this part will be used and extended throughout the whole monograph, and in particular to SINR based
models. Note however that these classical stochastic models can be used in a variety of contexts which
go far beyond the modeling of wireless networks. Chapter 1 reviews the definition and basic properties of
Poisson point processes in Euclidean space. We review key operations on Poisson point processes (thinning,
superposition, displacement) as well as key formulas like Campbell’s formula. Chapter 2 is focused on
properties of the spatial shot-noise process: its continuity properties, its Laplace transform, its moments
etc. Both additive and max shot-noise processes are studied. Chapter 3 bears on coverage processes,
and in particular on the Boolean model. Its basic coverage characteristics are reviewed. We also give a
brief account of its percolation properties. Chapter 4 studies random tessellations; the main focus is on
Poisson–Voronoi tessellations and cells. We also discuss various random objects associated with bivariate
point processes such as the set of points of the first point process that fall in a Voronoi cell w.r.t. the second
point process.
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
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