This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

Network Information Theory

Information Theory and Reliable Communication

The ability to manage knowledge has become increasingly important in today's knowledge economy. Knowledge is considered a valuable commodity, embedded in products and in the tacit knowledge of highly mobile individual employees. Knowledge management (KM) represents a deliberate and systematic approach to cultivating and sharing an organization's knowledge base. It is a highly multidisciplinary field that encompasses both information technology and intellectual capital. This textbook and professional reference offers a comprehensive overview of the field of KM, providing both a substantive theoretical grounding and a pragmatic approach to applying key concepts. Drawing on ideas, tools, and techniques from such disciplines as sociology, cognitive science, organizational behavior, and information science, the text describes KM theory and practice at the individual, community, and organizational levels. It offers illuminating case studies and vignettes from companies including IBM, Xerox, British Telecommunications, JP Morgan Chase, and Nokia. This second edition has been updated and revised throughout. New material has been added on the information and library science perspectives, taxonomies and knowledge classification, the media richness of the knowledge-sharing channel, e-learning, social networking in KM contexts, strategy tools, results-based outcome assessments, knowledge continuity and organizational learning models, KM job descriptions, copyleft and Creative Commons, and other topics. New case studies and vignettes have been added; and the references and glossary have been updated and expanded.

http://msjavan.persiangig.com/document/Research2/km%20book.pdf

Knowledge Management in Theory and Practice

In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Flows in Networks

Linear Regression Analysis

Active networks are network architectures with processors that are capable of executing code carried by the packets passing through them. A critical network management concern is the optimization of such networks and tight bounds on their performance serve as useful design benchmarks. A new bound on communication rates is developed that applies to network coding, which is a promising active network application that has processors transmit packets that are general functions, for example a bit-wise XOR, of selected received packets. The bound generalizes an edge-cut bound on routing rates by progressively removing edges from the network graph and checking whether certain strengthened d-separation conditions are satisfied. The bound improves on the cut-set bound and its efficacy is demonstrated by showing that routing is rate-optimal for some commonly cited examples in the networking literature.

/pdf/edge-cut-bounds-on-network-coding-rates-2ejx99349v.pdf

Edge-Cut Bounds on Network Coding Rates

The Lempel-Ziv codes are universal variable-to-fixed length codes that have become virtually standard in practical lossless data compression. For any given source output string from a Markov or unifilar source, we upper-bound the difference between the number of binary digits needed to encode the string and the self-information of the string. We use this result to demonstrate that for unifilar or Markov sources, the redundancy of encoding the first n letters of the source output with the Lempel-Ziv incremental parsing rule (LZ'78), the Welch modification (LZW), or a new variant is O((ln n)/sup -1/), and we upper-bound the exact form of convergence. We conclude by considering the relationship between the code length and the empirical entropy associated with a string.

/pdf/redundancy-of-the-lempel-ziv-incremental-parsing-rule-4ia9nei5bj.pdf

Redundancy of the Lempel-Ziv incremental parsing rule

gree than the retained sequences (“exons”) We have applied the information theoretic notion of entropy to characterize DNA sequences We consider a genetic sequence signal that is too small for asymptotic entropy estimates to be accurate, and for which similar approaches have previously failed We prove that the match length entropy estimator has a relatively fast converge rate and demonstrate experimentally that by using this entropy estimator, we can indeed extract a meaningful signal from segments of DNA Further, we derive a method for detecting certain signals within DNA known as splice junctions with significantly better performance than previously known methods

On the entropy of DNA: algorithms and measurements based on memory and rapid convergence

Tunstall codes are variable-to-fixed length codes that maximize the expected number of source letters per dictionary string for discrete, memoryless sources. We analyze a generalization of Tunstall coding to sources with memory and demonstrate that as the dictionary size increases, the number of code letters per source symbol comes arbitrarily close to the minimum among all variable-to-fixed length codes of the same size. We also find the asymptotic relationship between the dictionary size and the average length of a dictionary entry.

Generalized Tunstall codes for sources with memory

A rate distortion problem is solved that is motivated by a quantum data compression problem The goal is to send information about a source string x so that a receiver can construct a second string y for which the joint empirical probability distribution of x and y is close to some desired distribution The problem differs from the usual rate distortion problems in that one must consider both remote sources and distortion functions that are not averages of per-letter distortion functions

Serap A. Savari

Papers

Edge-Cut Bounds on Network Coding Rates

Redundancy of the Lempel-Ziv incremental parsing rule

On the entropy of DNA: algorithms and measurements based on memory and rapid convergence

Generalized Tunstall codes for sources with memory

Communicating Probability Distributions