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1413 results in Ebooks in network science

Page 54 of 71

  • 14 - Joint Source–Channel Coding

  • from Part II - Single-Hop Networks
  • Abbas El Gamal, Young-Han Kim, University of California, San Diego
  • Book:
    Network Information Theory
    Published online:
    05 June 2012
    Print publication:
    08 December 2011, pp 336-360

  • Summary

    In Chapters 4 through 9, we studied reliable communication of independent messages over noisy single-hop networks (channel coding), and in Chapters 10 through 13, we studied the dual setting of reliable communication of uncompressed sources over noiseless single-hop networks (source coding). These settings are special cases of the more general information flow problem of reliable communication of uncompressed sources over noisy single-hop networks. As we have seen in Section 3.9, separate source and channel coding is asymptotically sufficient for communicating a DMS over a DMC. Does such separation hold in general for communicating a k-DMS over a DM single-hop network?

    In this chapter, we show that such separation does not hold in general. Thus in some multiuser settings it is advantageous to perform joint source–channel coding. We demonstrate this breakdown in separation through examples of lossless communication of a 2-DMS over a DM-MAC and over a DM-BC.

    For the DM-MAC case, we show that joint source–channel coding can help communication by utilizing the correlation between the sources to induce statistical cooperation between the transmitters. We present a joint source–channel coding scheme that outperforms separate source and channel coding. We then show that this scheme can be improved when the sources have a common part, that is, a source that both senders can agree on with probability one.

    For the DM-BC case, we show that joint source–channel coding can help communication by utilizing the statistical compatibility between the sources and the channel. We first consider a separate source and channel coding scheme based on the Gray–Wyner source coding system and Marton's channel coding scheme. The optimal rate–region for the Gray–Wyner system naturally leads to several definitions of common information between correlated sources. We then describe a joint source–channel coding scheme that outperforms the separate Gray–Wyner and Marton coding scheme.

    Finally, we present a general single-hop network that includes as special cases many of themultiuser source and channel settings we discussed in previous chapters. We describe a hybrid source–channel coding scheme for this network.

  • 15 - Graphical Networks

  • from Part III - Multihop Networks
  • Abbas El Gamal, Young-Han Kim, University of California, San Diego
  • Book:
    Network Information Theory
    Published online:
    05 June 2012
    Print publication:
    08 December 2011, pp 363-381

  • Summary

    So far we have studied single-hop networks in which each node is either a sender or a receiver. In this chapter, we begin the discussion of multihop networks, where some nodes can act as both senders and receivers and hence communication can be performed over multiple rounds. We consider the limits on communication of independent messages over networks modeled by a weighted directed acyclic graph. This network model represents, for example, a wired network or a wireless mesh network operated in time or frequency division, where the nodes may be servers, handsets, sensors, base stations, or routers. The edges in the graph represent point-to-point communication links that use channel coding to achieve close to error-free communication at rates below their respective capacities. We assume that each node wishes to communicate a message to other nodes over this graphical network. The nodes can also act as relays to help other nodes communicate their messages. What is the capacity region of this network?

    Although communication over such a graphical network is not hampered by noise or interference, the conditions on optimal information flow are not known in general. The difficulty arises in determining the optimal relaying strategies when several messages are to be sent to different destination nodes.

    We first consider the graphical multicast network, where a source node wishes to communicate a message to a set of destination nodes. We establish the cutset upper bound on the capacity and show that it is achievable error-free via routing when there is only one destination, leading to the celebrated max-flow min-cut theorem. When there are multiple destinations, routing alone cannot achieve the capacity, however. We show that the cutset bound is still achievable, but using more sophisticated coding at the relays. The proof of this result involves linear network coding in which the relays perform simple linear operations over a finite field.

    We then consider graphical networks with multiple independent messages. We show that the cutset bound is tight when the messages are to be sent to the same set of destination nodes (multimessage multicast), and is achieved again error-free using linear network coding. When each message is to be sent to a different set of destination nodes, however, neither the cutset bound nor linear network coding is optimal in general.

  • 24 - Networking and InformationTheory

  • from Part IV - Extensions
  • Abbas El Gamal, Young-Han Kim, University of California, San Diego
  • Book:
    Network Information Theory
    Published online:
    05 June 2012
    Print publication:
    08 December 2011, pp 600-620

  • Summary

    The source and network models we discussed so far capture many essential ingredients of real-world communication networks, including

  • • noise,

  • • multiple access,

  • • broadcast,

  • • interference,

  • • time variation and uncertainty about channel statistics,

  • • distributed compression and computing,

  • • joint source–channel coding,

  • • multihop relaying,

  • • node cooperation,

  • • interaction and feedback, and

  • • secure communication.

    • Although a general theory for information flow under these models remains elusive, we have seen that there are several coding techniques—some of which are optimal or close to optimal—that promise significant performance improvements over today's practice. Still, the models we discussed do not capture other key aspects of real-world networks.

  • • We assumed that data is always available at the communication nodes. In real-world networks, data is bursty and the nodes have finite buffer sizes.

  • • We assumed that the network has a known and fixed number of users. In real-world networks, users can enter and leave the network at will.

  • • We assumed that the network operation is centralized and communication over the network is synchronous. Many real-world networks are decentralized and communication is asynchronous.

  • • We analyzed performance assuming arbitrarily long delays. In many networking applications, delay is a primary concern.

  • • We ignored the overhead (protocol) needed to set up the communication as well as the cost of feedback and channel state information.

    • While these key aspects of real-world networks have been at the heart of the field of computer networks, they have not been satisfactorily addressed by network information theory, either because of their incompatibility with the basic asymptotic approach of information theory or because the resulting models are messy and intractable. There have been several success stories at the intersection of networking and network information theory, however. In this chapter we discuss three representative examples.

    We first consider the channel coding problem for a DMC with random data arrival. We show that reliable communication is feasible provided that the data arrival rate is less than the channel capacity. Similar results can be established for multiuser channels and multiple data streams. A key new ingredient in this study is the notion of queue stability.

Phylogenetic Networks
  • Concepts, Algorithms and Applications
  • Daniel H. Huson, Regula Rupp, Celine Scornavacca
  • Published online:
    05 August 2011
    Print publication:
    02 December 2010
  • The evolutionary history of species is traditionally represented using a rooted phylogenetic tree. However, when reticulate events such as hybridization, horizontal gene transfer or recombination are believed to be involved, phylogenetic networks that can accommodate non-treelike evolution have an important role to play. This book provides the first interdisciplinary overview of phylogenetic networks. Beginning with a concise introduction to both phylogenetic trees and phylogenetic networks, the fundamental concepts and results are then presented for both rooted and unrooted phylogenetic networks. Current approaches and algorithms available for computing phylogenetic networks from different types of datasets are then discussed, accompanied by examples of their application to real biological datasets. The book also summarises the algorithms used for drawing phylogenetic networks, along with the existing software for their computation and evaluation. All datasets, examples and other additional information and links are available from the book's companion website at www.phylogenetic-networks.org.

  • PART IV - MACROMOLECULES

  • Bernhard Ø. Palsson, University of California, San Diego
  • Book:
    Systems Biology: Simulation of Dynamic Network States
    Published online:
    05 August 2012
    Print publication:
    26 May 2011, pp 243-244

  • Summary

    In the fourth and final part of the book we develop MASS models that include macromolecular functions that are coupled with metabolites. Many proteins in cells bind to small molecules and the bound states of the proteins have different functions than the unbound forms.

    The simplest and perhaps best known case is that of oxygen binding to hemoglobin. DPG23 is a unique metabolite in the red cell that binds to hemoglobin and alters the oxygen-carrying capacity of hemoglobin. DPG23 is made by a simple bypass on glycolysis, and in Chapter 13 we couple this bypass to the glycolytic model developed earlier in Chapter 10.

    Furthermore, the kinases in glycolysis can bind to many metabolites. Some binding states inhibit enzyme catalysis and some activate function. In Chapter 14 we show how such regulatory models can be incorporated into the glycolytic model. With these examples, the reader should have the basic understanding of the procedure and data needs for building large-scale kinetic models in cell and molecular biology.

  • 1 - Introduction

  • Bernhard Ø. Palsson, University of California, San Diego
  • Book:
    Systems Biology: Simulation of Dynamic Network States
    Published online:
    05 August 2012
    Print publication:
    26 May 2011, pp 1-16

  • Summary

    Systems biology has been brought to the forefront of life-science-based research and development. The need for systems analysis is made apparent by the inability of focused studies to explain whole network, cell, or organism behavior, and the availability of component data is what is fueling and enabling the effort. This massive amount of experimental information is a reflection of the complex molecular networks that underlie cellular functions. Reconstructed networks represent a common denominator in systems biology. They are used for data interpretation, comparing organism capabilities, and as the basis for computing their functional states. The companion book [89] details the topological features and assessment of functional states of biochemical reaction networks and how these features are represented by the stoichiometric matrix. In this book, we turn our attention to the kinetic properties of the reactions that make up a network. We will focus on the formulation of dynamic simulators and how they are used to generate and study the dynamic states of biological networks.

    Biological networks

    Cells are made up of many chemical constituents that interact to form networks. Networks are fundamentally comprised of nodes (the compounds) and the links (chemical transformations) between them. The networks take on functional states that we wish to compute, and it is these physiological states that we observe. This text is focused on dynamic states of networks.

    There are many different kinds of biological network of interest, and they can be defined in different ways.

Page 54 of 71