18,122 research outputs found
Vector Network Coding Algorithms
We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algorithms optimize the length L while designing the coding matrices. These algorithms apply both for regular network graphs as well as linear deterministic networks
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
Polynomial time algorithms for multicast network code construction
The famous max-flow min-cut theorem states that a source node s can send information through a network (V, E) to a sink node t at a rate determined by the min-cut separating s and t. Recently, it has been shown that this rate can also be achieved for multicasting to several sinks provided that the intermediate nodes are allowed to re-encode the information they receive. We demonstrate examples of networks where the achievable rates obtained by coding at intermediate nodes are arbitrarily larger than if coding is not allowed. We give deterministic polynomial time algorithms and even faster randomized algorithms for designing linear codes for directed acyclic graphs with edges of unit capacity. We extend these algorithms to integer capacities and to codes that are tolerant to edge failures
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
Low Complexity Encoding for Network Codes
In this paper we consider the per-node run-time complexity of network multicast codes. We show that the randomized algebraic network code design algorithms described extensively in the literature result in codes that on average require a number of operations that scales quadratically with the blocklength m of the codes. We then propose an alternative type of linear network code whose complexity scales linearly in m and still enjoys the attractive properties of random algebraic network codes. We also show that these codes are optimal in the sense that any rate-optimal linear network code must have at least a linear scaling in run-time complexity
A Deterministic Polynomial--Time Algorithm for Constructing a Multicast Coding Scheme for Linear Deterministic Relay Networks
We propose a new way to construct a multicast coding scheme for linear
deterministic relay networks. Our construction can be regarded as a
generalization of the well-known multicast network coding scheme of Jaggi et
al. to linear deterministic relay networks and is based on the notion of flow
for a unicast session that was introduced by the authors in earlier work. We
present randomized and deterministic polynomial--time versions of our algorithm
and show that for a network with destinations, our deterministic algorithm
can achieve the capacity in uses of the
network.Comment: 12 pages, 2 figures, submitted to CISS 201
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