Joint identity-message coding

Keller, Lorenzo; Siavoshani, Mahdi Jafari; Fragouli, Christina; Argyraki, Katerina; Diggavi, Suhas

doi:10.1109/jsac.2010.100913

Cited by 6 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Formally, we rewrite the channel defined in (4) as 5 (13) where is a diagonal random matrix whose elements on its diagonal are either 1 or 0. We also assume that is large, and as a result the transfer matrix is full rank with high probability.…”

Section: B Extension To the Packet Erasure Networkmentioning

confidence: 99%

See 1 more Smart Citation

On the Capacity of Noncoherent Network Coding

Siavoshani

Mohajer

Fragouli

et al. 2011

IEEE Trans. Inform. Theory

View full text Add to dashboard Cite

Abstract-We consider the problem of multicasting information from a source to a set of receivers over a network where intermediate network nodes perform randomized linear network coding operations on the source packets. We propose a channel model for the noncoherent network coding introduced by Koetter and Kschischang in [6], that captures the essence of such a network operation, and calculate the capacity as a function of network parameters. We prove that use of subspace coding is optimal, and show that, in some cases, the capacity-achieving distribution uses subspaces of several dimensions, where the employed dimensions depend on the packet length. This model and the results also allow us to give guidelines on when subspace coding is beneficial for the proposed model and by how much, in comparison to a coding vector approach, from a capacity viewpoint. We extend our results to the case of multiple source multicast that creates a virtual multiple access channel.

show abstract

Section: B Extension To the Packet Erasure Networkmentioning

confidence: 99%

“…This results in a loss of information rate that can be significant with respect to the min-cut value. In particular, for wireless sensor networks, where communication is restricted to short packet lengths, the coding vector overhead can be a significant fraction of the overall packet length [27], [13].…”

mentioning

confidence: 99%

On the Capacity of Noncoherent Network Coding

Siavoshani

Mohajer

Fragouli

et al. 2011

IEEE Trans. Inform. Theory

View full text Add to dashboard Cite

show abstract

“…Note that in equation (13) all of the erasure events are captured by the erasure matrix E[t]. Moreover, the erasure pattern is important only up to determining the number of packets that the destination receives, since the transfer matrix G[t] is unknown and distributed uniformly at random over all full rank matrices.…”

Section: B Extension To the Packet Erasure Networkmentioning

confidence: 99%

“…This results in a loss of information rate that can be significant with respect to the min-cut value. In particular, in wireless networks such as sensor networks where communication is restricted to short packet lengths, the coding vector overhead can be a significant fraction of the overall packet length [27], [13].…”

Section: Introductionmentioning

confidence: 99%

On the Capacity of Non-Coherent Network Coding

Siavoshani¹,

Mohajer²,

Fragouli³

et al. 2010

Preprint

View full text Add to dashboard Cite

We consider the problem of multicasting information from a source to a set of receivers over a network where intermediate network nodes perform randomized network coding operations on the source packets. We propose a channel model for the non-coherent network coding introduced by Koetter and Kschischang in [6], that captures the essence of such a network operation, and calculate the capacity as a function of network parameters. We prove that use of subspace coding is optimal, and show that, in some cases, the capacity-achieving distribution uses subspaces of several dimensions, where the employed dimensions depend on the packet length. This model and the results also allow us to give guidelines on when subspace coding is beneficial for the proposed model and by how much, in comparison to a coding vector approach, from a capacity viewpoint. We extend our results to the case of multiple source multicast that creates a virtual multiple access channel 1 .

show abstract