14 research outputs found
Slepian-Wolf-Cover theorem for networks of channels
Given a general network of discrete memoryless independent channels with multiple supply nodes and a single sink node, a Slepian-Wolf-Cover type of problem of transmitting multiple correlated informations through the network from the supply nodes to the sink node is considered from the source-channel matching point of view. By introducing the concept of achievable network, we give the necessary and sufficient condition for a network to be achievable, while making full use of the polymatroidal property of the capacity function of the network as well as the co-polymatroidal property of the correlated sources
Non-Adaptive Distributed Compression in Networks
In this paper, we discuss non-adaptive distributed compression of inter-node
correlated real-valued messages. To do so, we discuss the performance of
conventional packet forwarding via routing, in terms of the total network load
versus the resulting quality of service (distortion level). As a better
alternative for packet forwarding, we briefly describe our previously proposed
one-step Quantized Network Coding (QNC), and make motivating arguments on its
advantage when the appropriate marginal rates for distributed source coding are
not available at the encoder source nodes. We also derive analytic guarantees
on the resulting distortion of our one-step QNC scenario. Finally, we conclude
the paper by providing a mathematical comparison between the total network
loads of one-step QNC and conventional packet forwarding, showing a significant
reduction in the case of one-step QNC.Comment: Submitted for 2013 IEEE International Symposium on Information Theor
The Finite Field Multi-Way Relay Channel with Correlated Sources: Beyond Three Users
The multi-way relay channel (MWRC) models cooperative communication networks
in which many users exchange messages via a relay. In this paper, we consider
the finite field MWRC with correlated messages. The problem is to find all
achievable rates, defined as the number of channel uses required per reliable
exchange of message tuple. For the case of three users, we have previously
established that for a special class of source distributions, the set of all
achievable rates can be found [Ong et al., ISIT 2010]. The class is specified
by an almost balanced conditional mutual information (ABCMI) condition. In this
paper, we first generalize the ABCMI condition to the case of more than three
users. We then show that if the sources satisfy the ABCMI condition, then the
set of all achievable rates is found and can be attained using a separate
source-channel coding architecture.Comment: Author's final version (to be presented at ISIT 2012
Multi-Way Relay Networks: Orthogonal Uplink, Source-Channel Separation and Code Design
We consider a multi-way relay network with an orthogonal uplink and
correlated sources, and we characterise reliable communication (in the usual
Shannon sense) with a single-letter expression. The characterisation is
obtained using a joint source-channel random-coding argument, which is based on
a combination of Wyner et al.'s "Cascaded Slepian-Wolf Source Coding" and
Tuncel's "Slepian-Wolf Coding over Broadcast Channels". We prove a separation
theorem for the special case of two nodes; that is, we show that a modular code
architecture with separate source and channel coding functions is
(asymptotically) optimal. Finally, we propose a practical coding scheme based
on low-density parity-check codes, and we analyse its performance using
multi-edge density evolution.Comment: Authors' final version (accepted and to appear in IEEE Transactions
on Communications
Practical algorithms for gathering stored correlated data in a network
Many sensing systems remotely monitor/measure an environment at several sites, and then report these observations to a central site. We propose and investigate several practical algorithms for joint routing and compression of data files as they are forward from remote nodes to a central site, with the goal of minimizing the communication cost incurred. Our algorithms are practical in that they do not assume that nodes have a priori information about the correlation structure (and resulting compression gains) of the individual measurements at a given sensor or among multiple sensors. Instead, this correlation structure is learned as pieces of the files are routed and jointly compressed on their way to the sink, and routes are adaptively changed as the nodes learn more about the correlation structure of the data