This note will explore the basic
concepts of information theory. It is highly recommended for students planning
to delve into the fields of communications, data compression, and statistical
signal processing. Topics covered includes: Entropy and mutual information,
Chain rules and inequalities, Data processing, Fano's inequality, Asymptotic
equipartition property, Entropy rate, Source coding and Kraft inequality,
Optimal code length and roof code, Huffman codes, Shannon-Fano-Elias and
arithmetic codes, Maximum entropy, Channel capacity, Channel coding theorem,
Differential entropy, Gaussian channel, Parallel Gaussian channel and
water-filling, Quantization and rate-distortion.
The lecture notes
of Prof. Dr. rer. nat. Rudolf Mathar give a clear and very compact introduction
into information theory. These notes are divided into three key parts: the
basics of information theory, source coding, and information channels. The
introduction treats the basic notions and definitions in information theory in a
very solid way. Source coding gives methods and different techniques that are
used in encoding information, while the information channels section discusses
the pattern in which information is carried and noise that affects it. This
resource is a good pick for students and professionals who seek structure in the
principles of information theory and its applications from a respected expert in
the field.
Author(s): Prof. Dr. rer. nat. Rudolf Mathar, Institute
for Theoretical Information Technology Kopernikusstr, Germany
Om Carter-Introduction to information theory and entropy: It goes in deep
to do some basic concepts of information theory, focusing on the concept of
entropy and its applications. It does so by first investigating the measure of
complexity and the elementary theories of probability before introducing some
key ideas in information theory. It ranges from basic issues, such as entropy
theory and the Gibbs inequality, up to Shannon's communication theory but also
to practical applications in many diversified fields. Other topics dealt with
are Bayes Theorem, analog channels, the Maximum Entropy Principle, and
applications to biology and physics. The Kullback-Leibler information measure
will be discussed in trying to cast light upon quantification of information and
its relations with different fields of science. This book should be ideal for
the general reader interested in information theory and its immense areas of
application..
The lecture notes Advanced
Information Theory Notes by Prof. Dr. sc. techn. Gerhard Kramer cover advanced
topics in information theory. Information theory within the context of these
notes starts with discrete and continuous random variables to base the student
in deeper understandings of complicated scenarios. The key areas include channel
coding, important for good data transmission; typical sequences and sets, which
are fundamental in the theoretical and practical applications of the coding. The
text also explores lossy source coding and distributed source coding, which look
into how data might be compressed and transmitted with much efficiency. It also
covers multiaccess channels, an important aspect in showing just how different
sources of data interact. Such a broad-ranging textbook seems particularly
suited to readers having a firm grounding in basic information theory, wanting
to advance into more advanced areas as well as applications.
This
is a wide-ranging text by Shlomo Shamai and Abdellatif Zaidi, covering both
foundational and advanced topics in information theory applied to data
communications and processing. It discusses basic issues, such as information
bottleneck problems, unsupervised clustering via methods of the variational
information bottleneck, and rate-distortion analysis. It proceeds to get into
subjects of a higher level of difficulty: non-orthogonal eMBB and URLLC radio
access, robust baseband compression techniques, and amplitude-constrained MIMO
channels. Efficient algorithms have been derived for multicasting, content
placement in cache networks, and the fundamental limits of caching. The title
will be a ready reference for researchers and practitioners interested in the
theory and practice of modern communication systems, comprehensively covering
recent advancement efforts and applications in information theory.
This is a PDF document written by
J.G. Daugman on the fundamentals of the theory of information and coding.
Beginning with the very basic concept of probability and uncertainty, and the
concept of information, it arrives at entropies and their meaning. It deals with
the source coding theorems: prefix, variable-length, and fixed-length codes. It
looks into several kinds of channels, their properties, noise, and channel
capacity. The further topics delve into detail with continuous information,
noisy channel coding theorems, Fourier series elaborated on in making matters of
convergence, orthogonal representation, and useful Fourier theorems. The text
also expands into aspects such as sampling and aliasing, DFT, FFT algorithms,
and the quantized degrees-of-freedom in continuous signals and concludes with
discussions on the Gabor-Heisenberg-Weyl uncertainty relation and Kolmogorov
complexity for a general overview of some of the key principles of information
theory and coding.
This set of lecture notes by Venkatesan Guruswami
and Mahdi Cheraghchi addresses the intersection of information theory and
theoretical computer science. The core topics to be covered in the lecture note
include entropy, Kraft's inequality, source coding theorem, conditional entropy,
and mutual information. It also covers KL-divergence, Chernoff bounds, data
processing, and Fano's inequalities. Key concepts include AEP, universal source
coding using the Lempel-Ziv algorithm, and proof of its optimality. It covers
discrete channels and channel capacity, the Noisy Channel Coding Theorem, and
how to construct capacity-achieving codes by concatenation and by polar codes.
Additional topics: Bregman's theorem, Shearer's Lemma, graph entropy, and
applications to optimal set disjointness lower bounds. This text offers a
wide-ranging view of how the basic principles of information theory shed light
on the construction of algorithms, and the establishment of bounds-on the
complexity of problems in the field of theoretical computation.
Author(s): Venkatesan
Guruswami and Mahdi Cheraghchi