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Practical source-network decoding
- in ISWCS 2009, 2009
"... Abstract—When correlated sources are to be communicated over a network to more than one sink, joint source-network coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is r ..."
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Cited by 7 (2 self)
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Abstract—When correlated sources are to be communicated over a network to more than one sink, joint source-network coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is required to perform joint source-network decoding which is computationally expensive. Focusing on maximum a-posteriori decoders (or, in the case of continuous sources, conditional mean estimators), we show how to exploit (structural) knowledge about the network topology as well as the source correlations giving rise to an efficient decoder implementation (in some cases even with linear dependency on the number of nodes). In particular, we show how to statistically represent the overall system (including the packets) by a factor-graph on which the sum-product algorithm can be run. A proof-of-concept is provided in the form of a working decoder for the case of three sources and two sinks. I.
On distributed distortion optimization for correlated sources
- in Proc. of IEEE International Symposium on Information Theory
, 2007
"... Abstract — We consider lossy data compression in capacityconstrained networks with correlated sources. We develop, using dual decomposition, a distributed algorithm that maximizes an aggregate utility measure defined in terms of the distortion levels of the sources. No coordination among sources is ..."
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Cited by 5 (2 self)
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Abstract — We consider lossy data compression in capacityconstrained networks with correlated sources. We develop, using dual decomposition, a distributed algorithm that maximizes an aggregate utility measure defined in terms of the distortion levels of the sources. No coordination among sources is required; each source adjusts its distortion level according to distortion prices fed back by the sinks. The algorithm is developed for the case of squared error distortion and high resolution coding where the rate distortion region is known, and is easily extended to consider achievable regions that can be expressed in a related form. Our distributed optimization framework applies to unicast and multicast with and without network coding. Numerical example shows relatively fast convergence, allowing the algorithm to be used in time-varying networks. I.
Minimum Cost Mirror Sites Using Network Coding: Replication versus Coding at the Source Nodes
, 2011
"... Content distribution over networks is often achieved by using mirror sites that hold copies of files or portions thereof to avoid congestion and delay issues arising from excessive demands to a single location. Accordingly, there are distributed storage solutions that divide the file into pieces an ..."
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Cited by 3 (0 self)
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Content distribution over networks is often achieved by using mirror sites that hold copies of files or portions thereof to avoid congestion and delay issues arising from excessive demands to a single location. Accordingly, there are distributed storage solutions that divide the file into pieces and place copies of the pieces (replication) or coded versions of the pieces (coding) at multiple source nodes. We consider a network which uses network coding for multicasting the file. There is a set of source nodes that contains either subsets or coded versions of the pieces of the file. The cost of a given storage solution is defined as the sum of the storage cost and the cost of the flows required to support the multicast. Our interest is in finding the storage capacities and flows at minimum combined cost. We formulate the corresponding optimization problems by using the theory of information measures. In particular, we show that when there are two source nodes, there is no loss in considering subset sources. For three source nodes, we derive a tight upper bound on the cost gap between the coded and uncoded cases. We also present algorithms for determining the content of the source nodes.
Wireless Inter-Session Network Coding- An Approach Using Virtual Multicasts
"... Abstract—This paper addresses the problem of inter-session network coding to maximize throughput for multiple communication sessions in wireless networks. We introduce virtual multicast connections which can extract packets from original sessions and code them together. Random linear network codes c ..."
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Abstract—This paper addresses the problem of inter-session network coding to maximize throughput for multiple communication sessions in wireless networks. We introduce virtual multicast connections which can extract packets from original sessions and code them together. Random linear network codes can be used for these virtual multicasts. The problem can be stated as a flowbased convex optimization problem with side constraints. The proposed formulation provides a rate region which is at least as large as the region without inter-session network coding. We show the benefits of our technique for several scenarios by means of simulation. I.
Polymatroids with Network Coding
"... Abstract—The problem of network coding for mul-ticasting a single source to multiple sinks has first been studied by Ahlswede, Cai, Li and Yeung in 2000, in which they have established the celebrated max-flow mini-cut theorem on non-physical information flow over a network of independent channels. O ..."
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Abstract—The problem of network coding for mul-ticasting a single source to multiple sinks has first been studied by Ahlswede, Cai, Li and Yeung in 2000, in which they have established the celebrated max-flow mini-cut theorem on non-physical information flow over a network of independent channels. On the other hand, in 1980, Han has studied the case with correlated multiple sources and a single sink from the viewpoint of polymatroidal functions in which a nec-essary and sufficient condition has been demonstrated for reliable transmission over the network. This pa-per presents an attempt to unify both cases, which leads to establish a necessary and sufficient condition for reliable transmission over a noisy network for multicasting all the correlated multiple sources to all the multiple sinks. Furthermore, we address also the problem of transmitting “independent ” sources over a multiple-access-type of network as well as over a broadcast-type of network, which reveals that the (co-) polymatroidal structures are intrinsically involved in these types of network coding. 1.
0 Multicasting Correlated Multiple Sources to Multiple Sinks over a Noisy Network†
, 2010
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Efficient Joint Network-Source Coding for Multiple Terminals with Side Information∗
, 2011
"... Consider the problem of source coding in networks with multiple receiving termi-nals, each having access to some kind of side information. In this case, standard coding techniques are either prohibitively complex to decode, or require network-source cod-ing separation, resulting in sub-optimal trans ..."
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Consider the problem of source coding in networks with multiple receiving termi-nals, each having access to some kind of side information. In this case, standard coding techniques are either prohibitively complex to decode, or require network-source cod-ing separation, resulting in sub-optimal transmission rates. To alleviate this problem, we offer a joint network-source coding scheme based on matrix sparsification at the code design phase, which allows the terminals to use an efficient decoding procedure (syndrome decoding using LDPC), despite the network coding throughout the net-work. Via a novel relation between matrix sparsification and rate-distortion theory, we give lower and upper bounds on the best achievable sparsification performance. These bounds allow us to analyze our scheme, and, in particular, show that in the limit where all receivers have comparable side information (in terms of conditional entropy), or, equivalently, have weak side information, a vanishing density can be achieved. As a result, efficient decoding is possible at all terminals simultaneously. Simulation results motivate the use of this scheme at non-limiting rates as well.
Reliable Network Coding
, 2012
"... Network coding [2, 42], and more specifically random linear network coding [39, 13, 26, 28], is a powerful tool for delivering information across a network. Random coding techniques may be implemented in a distributed way within network elements, independently of the structure of the network. In [28 ..."
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Network coding [2, 42], and more specifically random linear network coding [39, 13, 26, 28], is a powerful tool for delivering information across a network. Random coding techniques may be implemented in a distributed way within network elements, independently of the structure of the network. In [28], it has been shown that the max-flow capacity of the network can be reached with probability exponentionally approaching one with the size of the Galois field in which the random coding operations are performed. This work has led to a number of practical schemes such as COPE, ANC, MIXIT, and MORE, etc. [35, 34, 33]. Nevertheless, network coding is very sensitive to transmission errors, packet losses, and corrupted packets which are intentionally injected by malicious nodes. Recombinations carried out by each node lead to a progressive con-tamination of the set of clean packets by the erroneous ones, which makes the decoding impossible at the receiver side. On the other hand, even in the ab-sence of errors, losses of packets lead to an insufficient number of packets at the
