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- Tracey Ho, Muriel Médard, +4 authors Ben Leong
- IEEE Transactions on Information Theory
- 2006

We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code… (More)

— We present a novel randomized network coding approach for robust, distributed transmission and compression of information in networks, and demonstrate its advantages over routing-based approaches. We present a randomized network coding approach for robust , distributed transmission and compression of information in networks. Network nodes transmit on each… (More)

- Sidharth Jaggi, Peter Sanders, +4 authors Ludo M. G. M. Tolhuizen
- IEEE Transactions on Information Theory
- 2005

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… (More)

We consider a randomized network coding approach for multicasting from several sources over a network, in which nodes independently and randomly select linear mappings from inputs onto output links over some field. This approach was first described in [3], which gave, for acyclic delay-free networks, a bound on error probability, in terms of the number of… (More)

- Amir F. Dana, Radhika Gowaikar, R. Palanki, Babak Hassibi, Michelle Effros
- IEEE Transactions on Information Theory
- 2006

In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring each node to send the same signal on all outgoing channels.… (More)

- Desmond S. Lun, Muriel Médard, Ralf Koetter, Michelle Effros
- Physical Communication
- 2008

— We present a capacity-achieving coding scheme for unicast or multicast over lossy packet networks. In the scheme, intermediate nodes perform additional coding yet do not decode nor even wait for a block of packets before sending out coded packets. Rather, whenever they have a transmission opportunity, they send out coded packets formed from random linear… (More)

- Minkyu Kim, Muriel Médard, +4 authors Michelle Effros
- IEEE INFOCOM 2007 - 26th IEEE International…
- 2007

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… (More)

— We consider the problem of minimizing the resources used for network coding while achieving the desired throughput in a multicast scenario. Since this problem is NP-hard, we seek a method for quickly finding sufficiently good solutions. To this end, we take an evolutionary approach based on a genetic algorithm that works in an algebraic framework,… (More)

- Aditya Ramamoorthy, Kamal Jain, Philip A. Chou, Michelle Effros
- IEEE Transactions on Information Theory
- 2006

This correspondence considers the problem of distributed source coding of multiple sources over a network with multiple receivers. Each receiver seeks to reconstruct all of the original sources. The work by Ho et al. 2004 demonstrates that random network coding can solve this problem at the potentially high cost of jointly decoding the source and the… (More)

- Ralf Koetter, Michelle Effros, Muriel Médard
- IEEE Trans. Information Theory
- 2011

—A family of equivalence tools for bounding network capacities is introduced. Given a network with node set , the capacity of is a set of non-negative vectors with elements corresponding to all possible multicast connections in ; a vector is in the capacity region for if and only if it is possible to simultaneously and reliably establish all multicast… (More)