Abstract
The min-cut, max-flow theorem states that a source node can send a commodity through a network to a sink node at the rate determined by the flow of the min-cut separating the source and the sink. Recently it has been shown that by linear re-encoding at nodes in communication networks, the min-cut rate can be also achieved in multicasting to several sinks. In this paper we discuss connections between such coding schemes and convolutional codes. We propose a method to simplify the convolutional encoder design that is based on a subtree decomposition of the network line graph, describe the structure of the associated matrices, investigate methods to reduce decoding complexity and discuss possible binary implementation.
Original language | English (US) |
---|---|
Pages (from-to) | 661-666 |
Number of pages | 6 |
Journal | IEEE International Conference on Communications |
Volume | 2 |
DOIs | |
State | Published - 2004 |
Externally published | Yes |
Event | 2004 IEEE International Conference on Communications - Paris, France Duration: Jun 20 2004 → Jun 24 2004 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering