The field of digital communication has evolved rapidly in the past few
decades, with commercial applications proliferating in wireline communi-
cation networks (e.g., digital subscriber loop, cable, fiber optics), wireless
communication (e.g., cell phones and wireless local area networks), and stor-
age media (e.g., compact discs, hard drives). The typical undergraduate and
graduate student is drawn to the field because of these applications, but is
often intimidated by the mathematical background necessary to understand
communication theory.
Notwithstanding its infancy, wireless mesh networking (WMN) is a hot and
growing field. Wireless mesh networks began in the military, but have since
become of great interest for commercial use in the last decade, both in local
area networks and metropolitan area networks. The attractiveness of mesh
networks comes from their ability to interconnect either mobile or fixed
devices with radio interfaces, to share information dynamically, or simply to
extend range through multi-hopping.
Rapid growth of wireless communication services in recent decades has created
a huge demand of radio spectrum. Spectrum scarcity and utilization inefficiency
limit the development of wireless networks. Cognitive radio is a promising tech-
nology that allows secondary users to reuse the underutilized licensed spectrum of
primary users. The major challenge for spectrum sharing is to achieve high spectrum
efficiency while making non-intrusive access to the licensed bands. This requires in-
formation of availability and quality of channel resources at secondary transmitters,
however, is difficult to be obtained perfectly in practice.
Mobile operators must continuously pursue cost‐
effective and efficient solutions to meet the high data
demand requirements of their subscribers. Limited spectrum
allocations and non‐contiguous spectrum blocks continue
to pose challenges for mobile operators supporting large
data uploads and downloads across their networks. With the
increase in video and social media content, the challenges
have increased exponentially.
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.
The idea for this book was born during one of my project-related trips to the beautiful city
of Hangzhou in China, where in the role of Chief Architect I had to guide a team of very
young, very smart and extremely dedicated software developers and verification engineers.
Soon it became clear that as eager as the team was to jump into the coding, it did not have
any experience in system architecture and design and if I did not want to spend all my time in
constant travel between San Francisco and Hangzhou, the only option was to groom a number
of local junior architects. Logically, one of the first questions being asked by these carefully
selected future architects was whether I could recommend a book or other learning material
that could speed up the learning cycle. I could not. Of course, there were many books on
various related topics, but many of them were too old and most of the updated information
was either somewhere on the Internet dispersed between many sites and online magazines, or
buried in my brain along with many years of experience of system architecture.
When 3GPP started standardizing the IMS a few years ago, most analysts expected the
number of IMS deploymentsto grow dramatically as soon the initial IMS specifications were
ready (3GPP Release 5 was functionallyfrozenin the first half of 2002and completedshortly
after that). While those predictions have proven to be too aggressive owing to a number of
upheavals hitting the ICT (Information and Communications Technologies) sector, we are
now seeing more and more commercial IMS-based service offerings in the market. At the
time of writing (May 2008), there are over 30 commercial IMS networks running live traffic,
addingup to over10million IMS users aroundthe world; the IMS is beingdeployedglobally.
In addition, there are plenty of ongoing market activities; it is estimated that over 130 IMS
contracts have been awarded to all IMS manufacturers. The number of IMS users will grow
substantially as these awarded contracts are launched commercially. At the same time, the
number of IMS users in presently deployed networks is steadily increasing as new services
are introduced and operators running these networks migrate their non-IMS users to their
IMS networks.
Wireless communication has become increasingly important not only for professional appli-
cations but also for many fields in our daily routine and in consumer electronics. In 1990,
a mobile telephone was still quite expensive, whereas today most teenagers have one, and
they use it not only for calls but also for data transmission. More and more computers use
wireless local area networks (WLANs), and audio and television broadcasting has become
digital.
Wireless communications has become a field of enormous scientific and economic interest. Recent
success stories include 2G and 3G cellular voice and data services (e.g., GSM and UMTS), wireless
local area networks (WiFi/IEEE 802.11x), wireless broadband access (WiMAX/IEEE 802.16x), and
digital broadcast systems (DVB, DAB, DRM). On the physical layer side, traditional designs typically
assume that the radio channel remains constant for the duration of a data block. However, researchers
and system designers are increasingly shifting their attention to channels that may vary within a block.
In addition to time dispersion caused by multipath propagation, these rapidly time-varying channels
feature frequency dispersion resulting from the Doppler effect. They are, thus, often referred to as
being “doubly dispersive.”
