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2014
Network coding, a relatively new paradigm for transmitting information through communication networks, allowing intermediate nodes in the network to combine data received on separate incoming channels before transmitting on outgoing channels. When compared with traditional routing paradigms, network coding can result in benefits such as higher throughput, fault-tolerance, and security. In this paper, we focus on studying network coding properties on a specific class of graphs called grid graphs. Network coding properties are related to well-known Steiner properties in graphs. Specifically, Steiner properties of grid graphs are studied, because they model VLSI layout design, which makes network coding properties a natural extension of this investigation. In particular, we looked at the maximum size of a communication group that is possible in a grid graph, given a specific desired transmission rate. Letting ρ k (G) be the maximum fraction of nodes in graph G that can be included in a network coded multicast group with an integral flow of size k, we prove that ρ 2 (G) < 1, ρ 3 (G) < 1 2 , and ρ 4 (G) < 1 3. In the first two cases, we construct families of communication groups on grid graphs which approach these bounds. In the latter case, we present a family of communication groups approaching a density of 1 4 .
2004
In this paper, we consider the problem of information multicast, namely transmitting common information from a sender s to a set of receivers T , in a communication network. Conventionally, in a communication network such as the Internet, this is done by distributing information over a multicast distribution tree. The nodes of such a tree are required only to replicate and forward, i.e., route, information received. Recently, Ahlswede et al.
Electronic Colloquium on …, 2003
Traditionally, communication networks are composed of routing nodes, which relay and duplicate data. Work in recent years has shown that for the case of multicast, an improvement in both rate and code-construction complexity can be gained by replacing these routing nodes by linear coding nodes. These nodes transmit linear combinations of the inputs transmitted to them.
2006 IEEE Information Theory Workshop, 2006
We investigate the network coding problem in a certain class of minimal multicast networks. In a multicast coding network, a source S needs to deliver h symbols, or packets, to a set of destinations T over an underlying communication network modeled by a graph G. A coding network is said to be h-minimal if it can deliver h symbols from S to the destination nodes, while any proper subnetwork of G can deliver at most h − 1 symbols to the set of destination nodes. This problem is motivated by the requirement to minimize the amount of network resources allocated for a multicast connections.
New Directions in Wireless Communications Research, 2009
Network coding, introduced by Ahlswede et al. in their pioneering work [1], has generated considerable research interest in recent years, and numerous subsequent papers, e.g., , have built upon this concept. One of the main advantages of network coding over traditional routed networks is in the area of multicast, where common information is transmitted from a source node to a set of terminal nodes. Ahlswede et al. showed in [1] that network coding can achieve the maximum multicast rate, which is not achievable by routing alone. When coding is used to perform multicast, the problem of establishing minimum cost multicast connection is equivalent to two effectively decoupled problems: one of determining the subgraph to code over and the other of determining the code to use over that subgraph. The latter problem has been studied extensively in , and a variety of methods have been proposed, which include employing simple random linear coding at every node. Such random linear coding schemes are completely decentralized, requiring no coordination between nodes, and can operate under dynamic conditions . These papers, however, all assume the availability of dedicated network resources.
2004
We consider a multicast configuration with two sources, and translate the network code design problem to vertex coloring of an appropriately defined graph. This observation enables to derive code design algorithms and alphabet size bounds, as well as establish a connection with a number of well-known results from discrete mathematics that increase our insight in the different trade-offs possible for network coding.
2006
In the multicast network coding problem, a source needs to deliver packets to a set of terminals over an underlying communication network . The nodes of the multicast network can be broadly categorized into two groups. The first group incudes encoding nodes, i.e., nodes that generate new packets by combining data received from two or more incoming links. The second group includes forwarding nodes that can only duplicate and forward the incoming packets. Encoding nodes are, in general, more expensive due to the need to equip them with encoding capabilities. In addition, encoding nodes incur delay and increase the overall complexity of the network. Accordingly, in this paper, we study the design of multicast coding networks with a limited number of encoding nodes. We prove that in a directed acyclic coding network, the number of encoding nodes required to achieve the capacity of the network is bounded by 3 2 . Namely, we present (efficiently constructible) network codes that achieve capacity in which the total number of encoding nodes is independent of the size of the network and is bounded by 3 2 . We show that the number of encoding nodes may depend both on and by presenting acyclic coding networks that require ( 2 ) encoding nodes. In the general case of coding networks with cycles, we show that the number of encoding nodes is limited by the size of the minimum feedback link set, i.e., the minimum number of links that must be removed from the network in order to eliminate cycles. We prove that the number of encoding nodes is bounded by (2 + 1) 3 2 , where is the minimum size of a feedback link set. Finally, we observe that determining or even crudely approximating the minimum number of required encoding nodes is an -hard problem.
Networking, IEEE/ACM Transactions on, 2003
We take a new look at the issue of network capacity. It is shown that network coding is an essential ingredient in achieving the capacity of a network. Building on recent work by Li et al., who examined the network capacity of multicast networks, we extend the network coding framework to arbitrary networks and robust networking. For networks which are restricted to using linear network codes, we find necessary and sufficient conditions for the feasibility of any given set of connections over a given network. We also consider the problem of network recovery for nonergodic link failures. For the multicast setup we prove 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. The results are derived for both delay-free networks and networks with delays.
2007
Abstract The problem of network coding with two simple unicast sessions is considered for general directed acyclic graphs. An explicit graph-theoretic characterization is provided for the feasibility of whether two symbols at different sources can be simultaneously transmitted to the designated sinks via network coding. The existence of a routing scheme is equivalent to finding edge-disjoint paths.
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012
The encoding complexity of network coding for single multicast networks has been intensively studied from several aspects: e.g., the time complexity, the required number of encoding links, and the required field size for a linear code solution. However, these issues as well as the solvability are less understood for networks with multiple multicast sessions. Recently, Wang and Shroff showed that the solvability of networks with two unit-rate multicast sessions (2-URMS) can be decided in polynomial time . In this paper, we prove that for the 2-URMS networks: 1) the solvability can be determined with time O(|E|); 2) a solution can be constructed with time O(|E|); 3) an optimal solution can be obtained in polynomial time; 4) the number of encoding links required to achieve a solution is upper-bounded by max{3, 2N -2}; and 5) the field size required to achieve a linear solution is upper-bounded by max{2, ⌊ 2N -7/4 + 1/2⌋}, where |E| is the number of links and N is the number of sinks of the underlying network. Both bounds are shown to be tight.
International Teletraffic Congress, 2012
Network coding has been shown to be the solution that allows to reach the theoretical maximum throughput in a capacitated telecommunication network [1]. It has also been shown to be a very appealing and practical alternative to routing-based approaches to send traffic from sources (servers) to terminals (clients) for many different applications. However, the initial theoretical claim of throughput benefit remains relatively unclear, mainly because the multicast throughput maximization problem is difficult to solve (it is closely related to the fractional Steiner tree packing problem which is NP-hard). In this paper, we show that these optimization problems are still tractable even for instances with a significant size (up to 50 nodes and 300 edges). We also propose and solve the multicast maximum throughput problem with an additional constraint on the number of multicast trees. We apply our algorithms on large sets of randomly generated instances, mainly based on bidirected graphs, because they are the most relevant to model fixed telecommunication infrastructures. The main result of our intensive experimental study is that, in practice, network coding does not increase throughput compared to traditional multicast. Instances showing a throughput gain can only be generated somewhat artificially by imposing some structure or trying to maximize the throughput gap. However, when we limit the number of multicast trees, then, most of the times, very significant throughput gaps appeared. Since management constraints often impose on network administrators a very limited use of multicast trees, network coding appears clearly as a very nice alternative for delivering content to customers.
Foundations and Trends® in Communications and Information Theory, 2005
Store-and-forward had been the predominant technique for transmitting information through a network until its optimality was refuted by network coding theory. Network coding offers a new paradigm for network communications and has generated abundant research interest in information and coding theory, networking, switching, wireless communications, cryptography, computer science, operations research, and matrix theory. We review the foundational work that has led to the development of network coding theory and discuss the theory for the transmission from a single source node to other nodes in the network. A companion issue discusses the theory when there are multiple source nodes each intending to transmit to a different set of destination nodes. Publisher's Note References to 'Part I' and 'Part II' in this issue refer to Foundations and Trends R in Communications and Information Technology Volume 2 Numbers 4 and 5 respectively. 3 Cyclic Networks 3.1 Non-equivalence between local and global descriptions 3.2 Convolutional network code 3.3 Decoding of convolutional network code 4 Network Coding and Algebraic Coding 4.1 The combination network 4.2 The Singleton bound and MDS codes 4.3 Network erasure/error correction and error detection vii 4.4 Further remarks a summary of the literature (see page 135) in the form of a table according to the following categorization of topics: 1. Linear coding 2. Nonlinear coding 3. Random coding 4. Static codes 5. Convolutional codes 6. Group codes 7. Alphabet size 8. Code construction 9. Algorithms/protocols 10. Cyclic networks 11. Undirected networks 12. Link failure/Network management 13. Separation theorem 14. Error correction/detection 15. Cryptography 16. Multiple sources 17. Multiple unicasts 18. Cost criteria 19. Non-uniform demand 20. Correlated sources 21. Max-flow/cutset/edge-cut bound 22. Superposition coding 23. Networking 24. Routing 25. Wireless/satellite networks 26. Ad hoc/sensor networks 27. Data storage/distribution 28. Implementation issues 29. Matrix theory 30. Complexity theory 31. Graph theory 32. Random graph 33. Tree packing 1.2. Some examples 245 Fig. 1.1 Multicasting over a communication network. Assume that we multicast two data bits b 1 and b 2 from the source node S to both the nodes Y and Z in the acyclic network depicted by Figure 1.1(a). Every channel carries either the bit b 1 or the bit b 2 as indicated. In this way, every intermediate node simply replicates and sends out the bit(s) received from upstream. The same network as in Figure 1.1(a) but with one less channel appears in Figures 1.1(b) and (c), which shows a way of multicasting 3 bits b 1 , b 2 and b 3 from S to the nodes Y and Z in 2 time units. This
IEEE Transactions on Information Theory, 2005
The famous max-flow min-cut theorem states that a source node can send information through a network ( ) to a sink node at a rate determined by the min-cut separating and . 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.
IEEE Transactions on Information Theory, 2000
Consider a communication network in which certain source nodes multicast information to other nodes on the network in the multihop fashion where every node can pass on any of its received data to others. We are interested in how fast each node can receive the complete information, or equivalently, what the information rate arriving at each node is. Allowing a node to encode its received data before passing it on, the question involves optimization of the multicast mechanisms at the nodes. Among the simplest coding schemes is linear coding, which regards a block of data as a vector over a certain base field and allows a node to apply a linear transformation to a vector before passing it on. We formulate this multicast problem and prove that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.
Computing Research Repository, 2007
Random linear network coding is a particularly decentralized approach to the multicast problem. Use of random network codes introduces a non-zero probability however that some sinks will not be able to successfully decode the required sources. One of the main theoretical motivations for random network codes stems from the lower bound on the probability of successful decoding reported by Ho et. al. (2003). This result demonstrates that all sinks in a linearly solvable network can successfully decode all sources provided that the random code field size is large enough. This paper develops a new bound on the probability of successful decoding.
EURASIP Journal on Advances in Signal Processing, 2017
Future networks are expected to depart from traditional routing schemes in order to embrace network coding (NC)-based schemes. These have created a lot of interest both in academia and industry in recent years. Under the NC paradigm, symbols are transported through the network by combining several information streams originating from the same or different sources. This special issue contains thirteen papers, some dealing with design aspects of NC and related concepts (e.g., fountain codes) and some showcasing the application of NC to new services and technologies, such as data multi-view streaming of video or underwater sensor networks. One can find papers that show how NC turns data transmission more robust to packet losses, faster to decode, and more resilient to network changes, such as dynamic topologies and different user options, and how NC can improve the overall throughput. This issue also includes papers showing that NC principles can be used at different layers of the networks (including the physical layer) and how the same fundamental principles can lead to new distributed storage systems. Some of the papers in this issue have a theoretical nature, including code design, while others describe hardware testbeds and prototypes.
2009
In this work, we study the computational perspective of network coding, focusing on two issues. First, we address the computational complexity of finding a network code for acyclic multicast networks. Second, we address the issue of reducing the amount of computation performed by network nodes. In particular, we consider the problem of finding a network code with the minimum possible number of encoding nodes, i.e., nodes that generate new packets by performing algebraic operations on packets received over incoming links.
2012 Proceedings of IEEE Southeastcon, 2012
Traditional method of solving group communications problem is by placing a super source with unlimited bandwidth to all sources. In this paper, we show that this method cannot guarantee the fairness within different sources for routing. Also in certain scenarios, the method can lead to wrong conclusion of network achieving higher throughput than it can actually deliver. Two algorithms are presented, one for routing and one for network coding to guarantee that each source has the same fairness and get the sub-optimal throughput for group communications in undirected networks. The throughputs achieved using either of these algorithms (one for routing only and one for network coding) are much better than any current widely-used IP multicast protocols. Between the two proposed algorithms, the algorithm for network coding can have throughput benefit in some scenarios but not always. Here, we show through simulation that network coding does not have constant throughput benefit in undirected networks in group communications scenario with the consideration of fairness within different sources.
IEEE Transactions on Information Theory, 2011
The problem of serving multicast flows in a crossbar switch is considered. Intraflow linear network coding is shown to achieve a larger rate region than the case without coding. A traffic pattern is presented which is achievable with coding but requires a switch speedup when coding is not allowed. The rate region with coding can be characterized in a simple graph-theoretic manner, in terms of the stable set polytope of the "enhanced conflict graph". No such graph-theoretic characterization is known for the case of fanout-splitting without coding. The minimum speedup needed to achieve 100% throughput with coding is shown to be upper bounded by the imperfection ratio of the enhanced conflict graph, where the imperfection ratio measures a certain graph theoretic property of the given graph. When applied to K × N switches with unicasts and broadcasts only, this gives a bound of min(2K-1/K, 2N/N+1) on the speedup. This shows that speedup, which is usually implemented in hardware, can often be substituted by network coding, which can be done in software. Computing an offline schedule (using prior knowledge of the flow rates) is reduced to fractional weighted graph coloring. A graph-theoretic online scheduling algorithm (using only queue occupancy information) is also proposed, that stabilizes the queues for all rates within the rate region.
While network coding can be an efficient means of information dissemination in networks, it is highly susceptible to "pollution attacks," as the injection of even a single erroneous packet has the potential to corrupt each and every packet received by a given destination. Even when suitable errorcontrol coding is applied, an adversary can, in many interesting practical situations, overwhelm the error-correcting capability of the code. To limit the power of potential adversaries, a broadcast-mode transformation is introduced, in which nodes are limited to just a single (broadcast) transmission per generation. Under this broadcast transformation, the multicast capacity of a network is changed (in general reduced) from the number of edge-disjoint paths between source and sink to the number of internally-disjoint paths. In some interesting cases (in particular, in a class of networks introduced by Jain, Lovász and Chou), the network capacity is maintained in broadcast mode. This results in a significant achievable transmission rate for such networks, even in the presence of adversaries.
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