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Computation in Multicast Networks: Function Alignment and Converse Theorems (2012)

by Changho Suh, Naveen Goela, Michael Gastpar
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Computation Over Gaussian Networks With Orthogonal Components

by Sang-woon Jeon, Chien-yi Wang, Michael Gastpar , 2013
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Network Coding with Computation Alignment

by Naveen Goela, Changho Suh, Michael Gastpar , 2012
"... Determining the capacity of multi-receiver networks with arbitrary message demands is an open problem in the network coding literature. In this paper, we consider a multisource, multi-receiver symmetric deterministic network model parameterized by channel coefficients (inspired by wireless network ..."
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Determining the capacity of multi-receiver networks with arbitrary message demands is an open problem in the network coding literature. In this paper, we consider a multisource, multi-receiver symmetric deterministic network model parameterized by channel coefficients (inspired by wireless network flow) in which the receivers compute a sum of the symbols generated at the sources. Scalar and vector linear coding strategies are analyzed. It is shown that computation alignment over finite field vector spaces is necessary to achieve the computation capacities in the network. To aid in the construction of coding strategies, network equivalence theorems are established for the decomposition of deterministic models into elementary sub-networks. The linear coding capacity for computation is characterized for all channel parameters considered in the model for a countably infinite class of networks. The constructive coding schemes introduced herein for a specific class of networks provide an optimistic viewpoint for the application of structured codes in network communication.
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...ite class of networks. is plotted for different channel parameters α � m L Remark 1: For several types of multi-receiver networks, non-linear codes achieve higher rates than linear codes. However, in =-=[17]-=- we prove that Theorem 1 not only characterizes the linear coding capacity for computation, but indeed the full computation capacity over 0 ≤ α ≤ 1: Clin COMP L IV. PROOF OF THEOREM 1: PART I CCOMP = ...

Modern Low-Complexity Capacity-Achieving Codes for Network Communication

by Naveen Goela , 2013
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...ts x1. . . . . . . . . . . . . . . . . . . . 111vi List of Tables 3.1 P (n) e For Different Rate Pairs Achieved For The Blackwell Channel . . 17 6.1 Computation Capacity Results For (m,q,L) Networks =-=[87]-=- . . . . . . . . . 85 7.1 A “Hybrid” Linear Transform Network . . . . . . . . . . . . . . . . . . . . 102 8.1 Comparison of Reduced-Dimension Linear Transforms . . . . . . . . . . . . 114vii Acknowle...

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