Cognitive radios have become a vital solution that allows sharing of the scarce
frequency spectrum available for wireless systems. It has been demonstrated
that it can be used for future wireless systems as well as integrated into 4G/5G
wireless systems. Although there is a great amount of literature in the design of
cognitive radios from a system and networking point of view, there has been very
limited available literature detailing the circuit implementation of such systems.
Our textbook, Radio Frequency Integrated Circuit Design for Cognitive Radios, is
the first book to fill a disconnect in the literature between Cognitive Radio systems
and a detailed account of the circuit implementation and architectures required to
implement such systems. In addition, this book describes several novel concepts
that advance state-of-the-art cognitive radio systems.
Long-TermEvolution(LTE)isarguablyoneofthemostimportantstepsinthecurrentphaseof
the development of modern mobile communications. It provides a suitable base for enhanced
services due to increased data throughput and lower latency figures, and also gives extra
impetus to the modernization of telecom architectures. The decision to leave the circuit-
switched domainoutofthescope ofLTE/SAEsystem standardization might soundradical but
itindicatesthatthetelecomworldisgoingstronglyfortheall-IPconcept----andthedeployment
of LTE/SAE is concrete evidence of this global trend.
Welcome to the world of wireless communications and the logical extension
to the broadband architectures that are emerging as the future of the
industry. No aspect of communications will be untouched by the wireless
interfaces;no part of our working environment will be left untouched either.
As the world changes and the newer technologies emerge, we can expect to
see more in the line of untethered communications than in the wired inter-
faces.
The advent of modern wireless devices, such as smart phones and MID 1 terminals,
has revolutionized the way people think of personal connectivity. Such devices
encompass multiple applications ranging from voice and video to high-speed data
transfer via wireless networks. The voracious appetite of twenty-first century users
for supporting more wireless applications on a single device is ever increasing.
These devices employ multiple radios and modems that cover multiple frequency
bands and multiple standards with a manifold of wireless applications often running
simultaneously.
Over many years, RF-MEMS have been a hot topic in research at the technology
and device level. In particular, various kinds of mechanical Si-MEMS resonators
and piezoelectric BAW (bulk acoustic wave) resonators have been developed. The
BAW technology has made its way to commercial products for passive RF filters,
in particular for duplexers in RF transceiver front ends for cellular communica-
tions. Beyond their use in filters, micromachined resonators can also be used in
conjunction with active devices in innovative circuits and architectures.
The author’s group has developed various chipless RFID tags and reader architectures
at 2.45, 4–8, 24, and 60 GHz. These results were published extensively in the form of
books, book chapters, refereed conference and journal articles, and finally, as patent
applications. However, there is still room for improvement of chipless RFID sys-
tems. In this book, we proposed advanced techniques of chipless RFID systems that
supersede their predecessors in signal processing, tag design, and reader architecture.
Radio frequency identification (RFID) technology is witnessing a recent explosion of
development in both industry and academia. A number of applications include supply
chain management, electronic payments, RFID passports, environmental monitoring
and control, office access control, intelligent labels, target detection and tracking, port
management, food production control, animal identification, and so on. RFID is also
an indispensable foundation to realize the pervasive computing paradigm—“Internet of
things.” It is strongly believed that many more scenarios will be identified when the
principles of RFID are thoroughly understood, cheap components available, and when
RFID security is guaranteed.
The past decade has seen an explosion of machine learning research and appli-
cations; especially, deep learning methods have enabled key advances in many
applicationdomains,suchas computervision,speechprocessing,andgameplaying.
However, the performance of many machine learning methods is very sensitive
to a plethora of design decisions, which constitutes a considerable barrier for
new users. This is particularly true in the booming field of deep learning, where
human engineers need to select the right neural architectures, training procedures,
regularization methods, and hyperparameters of all of these components in order to
make their networks do what they are supposed to do with sufficient performance.
This process has to be repeated for every application. Even experts are often left
with tedious episodes of trial and error until they identify a good set of choices for
a particular dataset.
