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.
This note serves as a comprehensive guide to fundamental concepts in
information theory and coding. This pdf provides discrete probability theory,
information theory, and coding principles. Beginning with Shannon's measure of
information, then delves into the efficient coding of information, the
methodology of typical sequences is introduced, emphasizing the distinction
between lossy and lossless source encoding. The text also discusses coding for
noisy digital channels, block coding principles and tree and trellis coding
principles.
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.
It serves as a basis for
everything, from the very basics of thermodynamics and information theory to
thermodynamic potentials and distributions, principles of irreversibility, phase
space evolution, and beyond. The book informs the readers about the very basics
of information theory: basic notions, basic definitions, and applications. It
also offers a fresh perspective on the second law of thermodynamics and quantum
information, and insights into the modern view of how information theory is
intertwined with the laws of physics. This book will be very useful to anyone
who wants to gain an understanding of the basic issues in both thermodynamics
and information theory and their intersection in current usage.
Prof. Tsachy Weissman's
lecture notes are an excellent summary of the core topics in the subject of
information theory. The document initiates with a basic overview of entropy and
relative entropy, followed by mutual information and asymptotic equipartition
property. Further, it discusses communications theory, channel capacity, and the
method of types. It also covers key topics such as typicality-conditioned and
joint, lossy compression, and rate-distortion theory. The notes also include
joint source-channel coding, where there is quite a good grasp of the principles
and applications of information theory. These notes will be very helpful for
students and professionals looking forward to structured, comprehensive
knowledge about the subject.
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.