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The two-unicast problem
TL;DR: It is shown that despite its seeming simplicity, the two-unicast problem is a very difficult problem: the general network coding problem can be reduced to two- Unicorn, and non-Shannon inequalities are necessary for characterizing the capacity of general two- unicast networks.
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Abstract: We consider the communication capacity of wireline networks for a two-unicast traffic pattern. The network has two sources and two destinations with each source communicating a message to its own destination, subject to the capacity constraints on the directed edges of the network. We propose a simple outer bound for the problem that we call the Generalized Network Sharing (GNS) bound. We show this bound is the tightest edge-cut bound for two-unicast networks and is tight in several bottleneck cases, though it is not tight in general. We also show that the problem of computing the GNS bound is NP-complete. Finally, we show that despite its seeming simplicity, the two-unicast problem is as hard as the most general network coding problem. As a consequence, linear coding is insufficient to achieve capacity for general two-unicast networks, and non-Shannon inequalities are necessary for characterizing capacity of general two-unicast networks.
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Citations
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A Linear Network Code Construction for General Integer Connections Based on the Constraint Satisfaction Problem
TL;DR: This paper introduces linear network mixing coefficients for code constructions of general connections that generalize random linear network coding (RLNC) for multicast connections and presents a probabilistic distributed algorithm with almost sure convergence in finite time by applying Communication Free Learning (CFL).
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References
Linear network coding
TL;DR: This work forms this multicast problem and proves that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.
An algebraic approach to network coding
Ralf Koetter,Muriel Medard +1 more
TL;DR: For the multicast setup it is proved that there exist coding strategies that provide maximally robust networks and that do not require adaptation of the network interior to the failure pattern in question.
Gaussian Interference Channel Capacity to Within One Bit
TL;DR: The capacity of the two-user Gaussian interference channel has been open for 30 years and the best known achievable region is due to Han and Kobayashi as mentioned in this paper, but its characterization is very complicated.
1.6K
The Complexity of Multiterminal Cuts
TL;DR: It is shown that the problem becomes NP-hard as soon as $k=3$, but can be solved in polynomial time for planar graphs for any fixed $k$, if the planar problem is NP- hard, however, if £k$ is not fixed.
805
Insufficiency of linear coding in network information flow
TL;DR: It is shown that the network coding capacity of this counterexample network is strictly greater than the maximum linear coding capacity over any finite field, so the network is not even asymptotically linearly solvable.
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