136 research outputs found

    Alignment based Network Coding for Two-Unicast-Z Networks

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    In this paper, we study the wireline two-unicast-Z communication network over directed acyclic graphs. The two-unicast-Z network is a two-unicast network where the destination intending to decode the second message has apriori side information of the first message. We make three contributions in this paper: 1. We describe a new linear network coding algorithm for two-unicast-Z networks over directed acyclic graphs. Our approach includes the idea of interference alignment as one of its key ingredients. For graphs of a bounded degree, our algorithm has linear complexity in terms of the number of vertices, and polynomial complexity in terms of the number of edges. 2. We prove that our algorithm achieves the rate-pair (1, 1) whenever it is feasible in the network. Our proof serves as an alternative, albeit restricted to two-unicast-Z networks over directed acyclic graphs, to an earlier result of Wang et al. which studied necessary and sufficient conditions for feasibility of the rate pair (1, 1) in two-unicast networks. 3. We provide a new proof of the classical max-flow min-cut theorem for directed acyclic graphs.Comment: The paper is an extended version of our earlier paper at ITW 201

    The Balanced Unicast and Multicast Capacity Regions of Large Wireless Networks

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    We consider the question of determining the scaling of the n2n^2-dimensional balanced unicast and the n2nn 2^n-dimensional balanced multicast capacity regions of a wireless network with nn nodes placed uniformly at random in a square region of area nn and communicating over Gaussian fading channels. We identify this scaling of both the balanced unicast and multicast capacity regions in terms of Θ(n)\Theta(n), out of 2n2^n total possible, cuts. These cuts only depend on the geometry of the locations of the source nodes and their destination nodes and the traffic demands between them, and thus can be readily evaluated. Our results are constructive and provide optimal (in the scaling sense) communication schemes.Comment: 37 pages, 7 figures, to appear in IEEE Transactions on Information Theor

    A Study of Communication Networks through the Lens of Reduction

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    A central goal of information theory is to characterize the capacity regions of communication networks. Due to the difficulty of the general problem, research is primarily focused on families of problems defined by various classifiers. These classifiers include the channel transition function (i.e., noisy, deterministic, network coding), demand type (i.e., single-source, 2-unicast), network topology (i.e. acyclic network coding, index coding). To date, the families of networks that are fully solved remain limited. Moreover, results derived for one specific family often do not extend easily to other families of problems. Our work shifts from the traditional focus on solving example networks to one that builds connections between problem solutions so that we can say where and when solving a problem in one domain would also solve a corresponding problem in another domain. Central to our approach is a technique called "reduction", in which we connect the solutions and results of communication problems. We say that problem A reduces to problem B when A can be solved by first transforming it to B and then applying a solution for B. We focus on two notions of reduction: reduction in code design and reduction in capacity region. Our central results demonstrate reductions with respect to a variety of classifiers. We show that finding multiple multicast network capacity regions reduces to finding multiple unicast network capacity regions both when capacity is defined as the maximal rate over all possible codes and when capacity is defined as the optimal rate over linear codes. As a corollary to this result, we show that the same capacity reduction holds for when network types are limited to either network coding networks or index coding networks. In several instances, we show that a reduction in code design extends to a reduction in capacity region if and only if the edge removal conjecture holds. Here, the edge removal conjecture states that removing an edge of negligible capacity from a network does not change its capacity region. One of the key challenges in network coding research is how to handle networks containing cycles. As a result, many papers on network coding restrict attention to acyclic networks and some results derived for acyclic networks do not extend to networks containing cycles. We consider a streaming model for network communication where information is streamed to its destination under a constraint on maximal delay at the decoder. Restricting our attention to this scenario enables us to prove a code reduction from network coding to index coding in both acyclic and cyclic networks. Since index coding networks are acyclic, a consequence of this reduction is that under the streaming model, there is no fundamental difference between acyclic and cyclic networks.</p

    A Network Coding Approach to Loss Tomography

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    Network tomography aims at inferring internal network characteristics based on measurements at the edge of the network. In loss tomography, in particular, the characteristic of interest is the loss rate of individual links and multicast and/or unicast end-to-end probes are typically used. Independently, recent advances in network coding have shown that there are advantages from allowing intermediate nodes to process and combine, in addition to just forward, packets. In this paper, we study the problem of loss tomography in networks with network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities, and we show that it improves several aspects of tomography including the identifiability of links, the trade-off between estimation accuracy and bandwidth efficiency, and the complexity of probe path selection. We discuss the cases of inferring link loss rates in a tree topology and in a general topology. In the latter case, the benefits of our approach are even more pronounced compared to standard techniques, but we also face novel challenges, such as dealing with cycles and multiple paths between sources and receivers. Overall, this work makes the connection between active network tomography and network coding

    A Local Perspective on the Edge Removal Problem

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    The edge removal problem studies the loss in network coding rates that results when a network communication edge is removed from a given network. It is known, for example, that in networks restricted to linear coding schemes and networks restricted to Abelian group codes, removing an edge e^∗ with capacity R_(e^∗) reduces the achievable rate on each source by no more than R_(e^∗). In this work, we seek to uncover larger families of encoding functions for which the edge removal statement holds. We take a local perspective: instead of requiring that all network encoding functions satisfy certain restrictions (e.g., linearity), we limit only the function carried on the removed edge e^∗. Our central results give sufficient conditions on the function carried by edge e^∗ in the code used to achieve a particular rate vector under which we can demonstrate the achievability of a related rate vector once e^∗ is removed

    Coding for Security and Reliability in Distributed Systems

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    This dissertation studies the use of coding techniques to improve the reliability and security of distributed systems. The first three parts focus on distributed storage systems, and study schemes that encode a message into n shares, assigned to n nodes, such that any n - r nodes can decode the message (reliability) and any colluding z nodes cannot infer any information about the message (security). The objective is to optimize the computational, implementation, communication and access complexity of the schemes during the process of encoding, decoding and repair. These are the key metrics of the schemes so that when they are applied in practical distributed storage systems, the systems are not only reliable and secure, but also fast and cost-effective. Schemes with highly efficient computation and implementation are studied in Part I. For the practical high rate case of r ≤ 3 and z ≤ 3, we construct schemes that require only r + z XORs to encode and z XORs to decode each message bit, based on practical erasure codes including the B, EVENODD and STAR codes. This encoding and decoding complexity is shown to be optimal. For general r and z, we design schemes over a special ring from Cauchy matrices and Vandermonde matrices. Both schemes can be efficiently encoded and decoded due to the structure of the ring. We also discuss methods to shorten the proposed schemes. Part II studies schemes that are efficient in terms of communication and access complexity. We derive a lower bound on the decoding bandwidth, and design schemes achieving the optimal decoding bandwidth and access. We then design schemes that achieve the optimal bandwidth and access not only for decoding, but also for repair. Furthermore, we present a family of Shamir's schemes with asymptotically optimal decoding bandwidth. Part III studies the problem of secure repair, i.e., reconstructing the share of a (failed) node without leaking any information about the message. We present generic secure repair protocols that can securely repair any linear schemes. We derive a lower bound on the secure repair bandwidth and show that the proposed protocols are essentially optimal in terms of bandwidth. In the final part of the dissertation, we study the use of coding techniques to improve the reliability and security of network communication. Specifically, in Part IV we draw connections between several important problems in network coding. We present reductions that map an arbitrary multiple-unicast network coding instance to a unicast secure network coding instance in which at most one link is eavesdropped, or a unicast network error correction instance in which at most one link is erroneous, such that a rate tuple is achievable in the multiple-unicast network coding instance if and only if a corresponding rate is achievable in the unicast secure network coding instance, or in the unicast network error correction instance. Conversely, we show that an arbitrary unicast secure network coding instance in which at most one link is eavesdropped can be reduced back to a multiple-unicast network coding instance. Additionally, we show that the capacity of a unicast network error correction instance in general is not (exactly) achievable. We derive upper bounds on the secrecy capacity for the secure network coding problem, based on cut-sets and the connectivity of links. Finally, we study optimal coding schemes for the network error correction problem, in the setting that the network and adversary parameters are not known a priori.</p

    Network Coding in Distributed, Dynamic, and Wireless Environments: Algorithms and Applications

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    The network coding is a new paradigm that has been shown to improve throughput, fault tolerance, and other quality of service parameters in communication networks. The basic idea of the network coding techniques is to relish the "mixing" nature of the information flows, i.e., many algebraic operations (e.g., addition, subtraction etc.) can be performed over the data packets. Whereas traditionally information flows are treated as physical commodities (e.g., cars) over which algebraic operations can not be performed. In this dissertation we answer some of the important open questions related to the network coding. Our work can be divided into four major parts. Firstly, we focus on network code design for the dynamic networks, i.e., the networks with frequently changing topologies and frequently changing sets of users. Examples of such dynamic networks are content distribution networks, peer-to-peer networks, and mobile wireless networks. A change in the network might result in infeasibility of the previously assigned feasible network code, i.e., all the users might not be able to receive their demands. The central problem in the design of a feasible network code is to assign local encoding coefficients for each pair of links in a way that allows every user to decode the required packets. We analyze the problem of maintaining the feasibility of a network code, and provide bounds on the number of modifications required under dynamic settings. We also present distributed algorithms for the network code design, and propose a new path-based assignment of encoding coefficients to construct a feasible network code. Secondly, we investigate the network coding problems in wireless networks. It has been shown that network coding techniques can significantly increase the overall throughput of wireless networks by taking advantage of their broadcast nature. In wireless networks each packet transmitted by a device is broadcasted within a certain area and can be overheard by the neighboring devices. When a device needs to transmit packets, it employs the Index Coding that uses the knowledge of what the device's neighbors have heard in order to reduce the number of transmissions. With the Index Coding, each transmitted packet can be a linear combination of the original packets. The Index Coding problem has been proven to be NP-hard, and NP-hard to approximate. We propose an efficient exact, and several heuristic solutions for the Index Coding problem. Noting that the Index Coding problem is NP-hard to approximate, we look at it from a novel perspective and define the Complementary Index Coding problem, where the objective is to maximize the number of transmissions that are saved by employing coding compared to the solution that does not involve coding. We prove that the Complementary Index Coding problem can be approximated in several cases of practical importance. We investigate both the multiple unicast and multiple multicast scenarios for the Complementary Index Coding problem for computational complexity, and provide polynomial time approximation algorithms. Thirdly, we consider the problem of accessing large data files stored at multiple locations across a content distribution, peer-to-peer, or massive storage network. Parts of the data can be stored in either original form, or encoded form at multiple network locations. Clients access the parts of the data through simultaneous downloads from several servers across the network. For each link used client has to pay some cost. A client might not be able to access a subset of servers simultaneously due to network restrictions e.g., congestion etc. Furthermore, a subset of the servers might contain correlated data, and accessing such a subset might not increase amount of information at the client. We present a novel efficient polynomial-time solution for this problem that leverages the matroid theory. Fourthly, we explore applications of the network coding for congestion mitigation and over flow avoidance in the global routing stage of Very Large Scale Integration (VLSI) physical design. Smaller and smarter devices have resulted in a significant increase in the density of on-chip components, which has given rise to congestion and over flow as critical issues in on-chip networks. We present novel techniques and algorithms for reducing congestion and minimizing over flows
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