Homogeneous Partitioning of the Surveillance Volume discusses the
implementation of the first of three sequentially complementary approaches for
increasing the probability of target detection within at least some of the cells of
the surveillance volume for a spatially nonGaussian or Gaussian “noise”
environment that is temporally Gaussian. This approach, identified in the Preface
as Approach A, partitions the surveillance volume into homogeneous contiguous
subdivisions.
At the macroscopic level of system layout, the most important issue is path loss. In the
older mobile radio systems that are limited by receiver noise, path loss determines SNR and
the maximum coverage area. In cellular systems, where the limiting factor is cochannel
interference, path loss determines the degree to which transmitters in different cells interfere
with each other, and therefore the minimum separation before channels can be reused.
The ever-increasing demand for private and sensitive data transmission over wireless net-
works has made security a crucial concern in the current and future large-scale, dynamic,
and heterogeneous wireless communication systems. To address this challenge, computer
scientists and engineers have tried hard to continuously come up with improved crypto-
graphic algorithms. But typically we do not need to wait too long to find an efficient way
to crack these algorithms. With the rapid progress of computational devices, the current
cryptographic methods are already becoming more unreliable. In recent years, wireless re-
searchers have sought a new security paradigm termed physical layer security. Unlike the
traditional cryptographic approach which ignores the effect of the wireless medium, physi-
cal layer security exploits the important characteristics of wireless channel, such as fading,
interference, and noise, for improving the communication security against eavesdropping
attacks. This new security paradigm is expected to complement and significantly increase
the overall communication security of future wireless networks.
This book is a result of the recent rapid advances in two related technologies: com-
munications and computers. Over the past few decades, communication systems
have increased in complexity to the point where system design and performance
analysis can no longer be conducted without a significant level of computer sup-
port. Many of the communication systems of fifty years ago were either power or
noise limited. A significant degrading effect in many of these systems was thermal
noise, which was modeled using the additive Gaussian noise channel.
The investigation of the propagation channel is becoming more and more important in mod-
ern wireless communication. The demand for spectral efficiency motivates exploitation of
all channels that can possibly be used for communications. Nowadays, a common trend for
designing physical layer algorithms is to adapt the transceiving strategy, either by maximizing
the diversity gains or by utilizing the coherence of the channels to improve the signal-to-noise
power ratio.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
In this thesis several asp ects of space-time pro cessing and equalization for wire-
less communications are treated. We discuss several di?erent metho ds of improv-
ing estimates of space-time channels, such as temp oral parametrization, spatial
parametrization, reduced rank channel estimation, b o otstrap channel estimation,
and joint estimation of an FIR channel and an AR noise mo del. In wireless commu-
nication the signal is often sub ject to intersymb ol interference as well as interfer-
ence from other users.
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.
A wireless communication network can be viewed as a collection of nodes, located in some domain, which
can in turn be transmitters or receivers (depending on the network considered, nodes may be mobile users,
base stations in a cellular network, access points of a WiFi mesh etc.). At a given time, several nodes
transmit simultaneously, each toward its own receiver. Each transmitter–receiver pair requires its own
wireless link. The signal received from the link transmitter may be jammed by the signals received from
the other transmitters. Even in the simplest model where the signal power radiated from a point decays in
an isotropic way with Euclidean distance, the geometry of the locations of the nodes plays a key role since
it determines the signal to interference and noise ratio (SINR) at each receiver and hence the possibility of
establishing simultaneously this collection of links at a given bit rate. The interference seen by a receiver is
the sum of the signal powers received from all transmitters, except its own transmitter.
Recent advances in wireless communication technologies have had a transforma-
tive impact on society and have directly contributed to several economic and social
aspects of daily life. Increasingly, the untethered exchange of information between
devices is becoming a prime requirement for further progress, which is placing an
ever greater demand on wireless bandwidth. The ultra wideband (UWB) system
marks a major milestone in this progress. Since 2002, when the FCC allowed the
unlicensed use of low-power, UWB radio signals in the 3.1–10.6GHz frequency
band, there has been significant synergistic advance in this technology at the cir-
cuits, architectural and communication systems levels. This technology allows for
devices to communicate wirelessly, while coexisting with other users by ensuring
that its power density is sufficiently low so that it is perceived as noise to other
users.