5,914 research outputs found
Path Gain Algebraic Formulation for the Scalar Linear Network Coding Problem
In the algebraic view, the solution to a network coding problem is seen as a
variety specified by a system of polynomial equations typically derived by
using edge-to-edge gains as variables. The output from each sink is equated to
its demand to obtain polynomial equations. In this work, we propose a method to
derive the polynomial equations using source-to-sink path gains as the
variables. In the path gain formulation, we show that linear and quadratic
equations suffice; therefore, network coding becomes equivalent to a system of
polynomial equations of maximum degree 2. We present algorithms for generating
the equations in the path gains and for converting path gain solutions to
edge-to-edge gain solutions. Because of the low degree, simplification is
readily possible for the system of equations obtained using path gains. Using
small-sized network coding problems, we show that the path gain approach
results in simpler equations and determines solvability of the problem in
certain cases. On a larger network (with 87 nodes and 161 edges), we show how
the path gain approach continues to provide deterministic solutions to some
network coding problems.Comment: 12 pages, 6 figures. Accepted for publication in IEEE Transactions on
Information Theory (May 2010
Linear Network Coding for Two-Unicast- Networks: A Commutative Algebraic Perspective and Fundamental Limits
We consider a two-unicast- network over a directed acyclic graph of unit
capacitated edges; the two-unicast- network is a special case of two-unicast
networks where one of the destinations has apriori side information of the
unwanted (interfering) message. In this paper, we settle open questions on the
limits of network coding for two-unicast- networks by showing that the
generalized network sharing bound is not tight, vector linear codes outperform
scalar linear codes, and non-linear codes outperform linear codes in general.
We also develop a commutative algebraic approach to deriving linear network
coding achievability results, and demonstrate our approach by providing an
alternate proof to the previous results of C. Wang et. al., I. Wang et. al. and
Shenvi et. al. regarding feasibility of rate in the network.Comment: A short version of this paper is published in the Proceedings of The
IEEE International Symposium on Information Theory (ISIT), June 201
Algebraic Network Coding Approach to Deterministic Wireless Relay Networks
The deterministic wireless relay network model, introduced by Avestimehr et
al., has been proposed for approximating Gaussian relay networks. This model,
known as the ADT network model, takes into account the broadcast nature of
wireless medium and interference. Avestimehr et al. showed that the Min-cut
Max-flow theorem holds in the ADT network.
In this paper, we show that the ADT network model can be described within the
algebraic network coding framework introduced by Koetter and Medard. We prove
that the ADT network problem can be captured by a single matrix, called the
"system matrix". We show that the min-cut of an ADT network is the rank of the
system matrix; thus, eliminating the need to optimize over exponential number
of cuts between two nodes to compute the min-cut of an ADT network.
We extend the capacity characterization for ADT networks to a more general
set of connections. Our algebraic approach not only provides the Min-cut
Max-flow theorem for a single unicast/multicast connection, but also extends to
non-multicast connections such as multiple multicast, disjoint multicast, and
two-level multicast. We also provide sufficiency conditions for achievability
in ADT networks for any general connection set. In addition, we show that the
random linear network coding, a randomized distributed algorithm for network
code construction, achieves capacity for the connections listed above.
Finally, we extend the ADT networks to those with random erasures and cycles
(thus, allowing bi-directional links). Note that ADT network was proposed for
approximating the wireless networks; however, ADT network is acyclic.
Furthermore, ADT network does not model the stochastic nature of the wireless
links. With our algebraic framework, we incorporate both cycles as well as
random failures into ADT network model.Comment: 9 pages, 12 figures, submitted to Allerton Conferenc
Optimization-Based Linear Network Coding for General Connections of Continuous Flows
For general connections, the problem of finding network codes and optimizing
resources for those codes is intrinsically difficult and little is known about
its complexity. Most of the existing solutions rely on very restricted classes
of network codes in terms of the number of flows allowed to be coded together,
and are not entirely distributed. In this paper, we consider a new method for
constructing linear network codes for general connections of continuous flows
to minimize the total cost of edge use based on mixing. We first formulate the
minimumcost network coding design problem. To solve the optimization problem,
we propose two equivalent alternative formulations with discrete mixing and
continuous mixing, respectively, and develop distributed algorithms to solve
them. Our approach allows fairly general coding across flows and guarantees no
greater cost than any solution without network coding.Comment: 1 fig, technical report of ICC 201
Evolutionary Approaches to Minimizing Network Coding Resources
We wish to minimize the resources used for network coding while achieving the
desired throughput in a multicast scenario. We employ evolutionary approaches,
based on a genetic algorithm, that avoid the computational complexity that
makes the problem NP-hard. Our experiments show great improvements over the
sub-optimal solutions of prior methods. Our new algorithms improve over our
previously proposed algorithm in three ways. First, whereas the previous
algorithm can be applied only to acyclic networks, our new method works also
with networks with cycles. Second, we enrich the set of components used in the
genetic algorithm, which improves the performance. Third, we develop a novel
distributed framework. Combining distributed random network coding with our
distributed optimization yields a network coding protocol where the resources
used for coding are optimized in the setup phase by running our evolutionary
algorithm at each node of the network. We demonstrate the effectiveness of our
approach by carrying out simulations on a number of different sets of network
topologies.Comment: 9 pages, 6 figures, accepted to the 26th Annual IEEE Conference on
Computer Communications (INFOCOM 2007
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