Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Acknowledgments
- Notation
- 1 Introduction
- Part I Preliminaries
- Part II Single-Hop Networks
- Part III Multihop Networks
- 15 Graphical Networks
- 16 Relay Channels
- 17 Interactive Channel Coding
- 18 DiscreteMemoryless Networks
- 19 Gaussian Networks
- 20 Compression over Graphical Networks
- Part IV Extensions
- Appendices
- Bibliography
- Common Symbols
- Author Index
- Subject Index
15 - Graphical Networks
from Part III - Multihop Networks
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Dedication
- Contents
- Preface
- Acknowledgments
- Notation
- 1 Introduction
- Part I Preliminaries
- Part II Single-Hop Networks
- Part III Multihop Networks
- 15 Graphical Networks
- 16 Relay Channels
- 17 Interactive Channel Coding
- 18 DiscreteMemoryless Networks
- 19 Gaussian Networks
- 20 Compression over Graphical Networks
- Part IV Extensions
- Appendices
- Bibliography
- Common Symbols
- Author Index
- Subject Index
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.
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- Information
- Network Information Theory , pp. 363 - 381Publisher: Cambridge University PressPrint publication year: 2011