Broadcast and slepian-wolf multicast over Aref networks

2009

Abstract The multicasting of an independent and identically distributed Gaussian source over a binary erasure broadcast channel is considered. This model applies to a one-to-many transmission scenario in which some mechanism at the physical layer delivers information packets with losses represented by erasures, and users are subject to different erasure probabilities.

2013 22nd International Conference on Computer Communication and Networks (ICCCN), 2013

This paper examines the problem of rate allocation for multicasting over slow Rayleigh fading channels using network coding. In the proposed model, the network is treated as a collection of Rayleigh fading multiple access channels. In this model, rate allocation scheme that is based solely on the statistics of the channels is presented. The rate allocation scheme is aimed at minimizing the outage probability. An upper bound is presented for the probability of outage in the fading multiple access channel. A suboptimal solution based on this bound is given. A distributed primal-dual gradient algorithm is derived to solve the rate allocation problem. data by network coding has been extensively extended by e.g. and it is well known that in order to achieve the multicast rate, a linear combination over a finite field suffices if the field size is larger than the number of destinations. Moreover, centralized linear network coding can be designed in polynomial time . Decentralized linear network coding can be implemented using a random code approach . A comprehensive survey of network coding can be found in e.g., .

Proceedings of the 4th International ICST Conference on Wireless Internet, 2008

Multicasting is the delivery of common information to multiple receivers. It finds its application in multi-media broadcasts, group communication in social networks etc. The multicast traffic in networks can constitute a significant portion of the total traffic (e.g. 80% in military communications) and hence it is imperative that they are served efficiently. So far "single rate" multicasting, where all receivers receive the data at a common rate from the source has attracted most of the attention . Yet, single-rate multicasting may yield low utilization of the network resources when a subset of the receivers creates a bottleneck for the whole multicast group. Multirate multicasting is a strategy where the source is allowed to multicast its data to different destinations at different rates based on the condition of the network to them. Multirate multicasting allows users with better channels to achieve maximum performance.

IEEE Data Compression Conference, 2008

This paper considers the problem of distributed scalable coding of corre-lated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the sensor sources and ...

2007

Abstract—In this paper, the problem of transmitting two symbols from two sources to multiple destinations using network coding is considered for general directed acyclic graphs. When all destination nodes are interested in both symbols, the feasibility of this problem is characterized by the classic min-cut maxflow theorem. This work focuses on the scenarios in which each destination is interested in one source symbol.

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

2011

In this paper, we give a characterization of the rate region for the degraded two message set problem, applied to a combination network with erasure channels. We also provide an algorithm that uses topological information in order to deliver the two messages to the receivers, and we show that our algorithm is optimal, in the sense that it achieves any rate pair in the region. We compare our algorithm analytically with a naive approach oblivious to the network structure, and we give an insight on what benefits should be expected for different classes of networks.

IEEE Transactions on Signal Processing, 2010

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit, a setting typically encountered in sensor networks. As communication channel conditions may vary with time, it is often desirable to guarantee a base layer of coarse information during channel fades, as well as robustness to channel link (or sensor) failures. The main contribution is twofold. First, we consider the special case of multistage distributed coding, and show that naive combination of distributed coding with multistage coding yields poor rate-distortion performance, due to underlying conflicts between the objectives of these two coding methods. An appropriate system paradigm is developed, which allows such tradeoffs to be explicitly controlled. Next, we consider the unconstrained scalable distributed coding problem. Although a standard "Lloyd-style" distributed coder design algorithm is easily generalized to encompass scalable coding, the algorithm performance is heavily dependent on initialization and will virtually always converge to a poor local minimum. We propose an effective initialization scheme for such a system, which employs a properly designed multistage distributed coder. We present iterative design techniques and derive the necessary conditions for optimality for both multistage and unconstrained scalable distributed coding systems. Simulation results show substantial gains for the proposed multistage distributed coding system over naive extensions which incorporate scalability in a multistage distributed coding system. Moreover, the proposed overall scalable distributed coder design consistently and substantially outperforms the randomly initialized "Lloyd-style" scalable distributed coder design.

2010 18th European Signal Processing Conference, 2010

Distributed source coding (DSC) refers to separate compression and joint decompression of mutually correlated sources. Though theoretical foundations were set more than thirty years ago, driven by applications such as wireless sensor networks, video surveillance, and multiview video, DSC has over the past few years become a very active research area. This paper provides an introduction to DSC theory, practical code designs and applications, and outlines current research trends while identifying challenges and opportunities in both theory and practice of DSC.

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.