networked slepian –wolf: theory, algorithms, and scaling laws
DESCRIPTION
Networked Slepian –Wolf: Theory, Algorithms, and Scaling Laws. R˘azvan Cristescu , Member, IEEE, Baltasar Beferull -Lozano, Member, IEEE, Martin Vetterli , Fellow, IEEE. IEEE Transactions on Information Theory, Dec., 2005. Outline. Introduction Slepian –Wolf Coding - PowerPoint PPT PresentationTRANSCRIPT
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Networked Slepian–Wolf: Theory, Algorithms, and Scaling Laws
R˘azvan Cristescu, Member, IEEE, Baltasar Beferull-Lozano, Member, IEEE, Martin Vetterli, Fellow, IEEE
IEEE Transactions on Information Theory, Dec., 2005
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Outline
• Introduction– Slepian–Wolf Coding
• Problem Formulation– Single Sink Case– Multiple Sink Case
• Single Sink Data Gathering• Multiple Sink Data Gathering– Heuristic Approximation Algorithms
• Numerical Simulations• Conclusion
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Introduction• Independent encoding/decoding
• Low coding gain• Optimal transmission structure: Shortest path tree
• Encoding with explicit communication– Nodes can exploit the data correlation only when the data of other nodes is locally
at them).– Without knowing the correlation among nodes a priori.
• Distributed source coding: Slepian–Wolf coding– Allow nodes to use joint coding of correlated data without explicit communication
• Assume a prior knowledge of global network structure and correlation structure is availlable• Exploiting data correlation without explicit communication (coding at each node Independent
ly)– Node can exploit data correlation among nodes without explicit communication.
• Optimal transmission structure: Shortest path tree
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Slepian–Wolf coding
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Slepian–Wolf coding
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Slepian–Wolf coding
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Slepian–Wolf coding
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Problem
Single Sink Case Multiple Sink Case
Assume the Slepian–Wolf coding is used. Then,
(1) Find a rate allocation that minimizes the total network cost.
(2) Find an optimal transmission structure.
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Preposition
• Proposition 1: Separation of source coding and transmission structure optimization.
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Single-Sink Data Gathering
• Optimal Transmission Structure: – Shortest Path Tree
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Single-Sink Data Gathering
Optimization problem
Rate Allocation
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Proof
),...,|( 11 XXXHR NNN
Consider that 11,...,, XXX NN with weights
),(...),(),( 11 SXdSXdSXd STPNSTPNSTP
Since
Thus, assigning ),...,|( 11 XXXHR NNN Yields optimal
),...,|,( 1211 XXXXHRR NNNNN
),...,|(),...,,|(
121
121
XXXHXXXXH
NN
NNN
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Rate Allocation
R1: the largest
R1: the smallest
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Example
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Multiple Sink Case
• For Node X3, the optimal transmission structure is the
minimum-weight tree rooted at X3 and span the sinks S1 and S2.
the minimum Steiner tree (NP-complete)
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Steiner Tree
• Euclidean Steiner tree problem– Given N points in the plane, it is required to connect them
by lines of minimum total length in such a way that any two points may be interconnected by line segments either directly or via other points and line segments.
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Steiner Tree
• Steiner tree in graphs– Given a weighted graph G(V, E, w) and a subset of
its vertices S V , find a tree of minimal weight which includes all vertices in S.
5
52
6
2
2
3
4
13
2
23 4
Terminal
Steiner points
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The Minimum Steiner Tree
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Existing Approximation• If the weights of the graph are the Euclidean distances,– the Euclidean Steiner tree problem– The existing approximation PTAS [3],
with approximation ratio (1+), > 0.
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Proposed Heuristic Approximation Algorithms
Assumption : Nodes that are outside k-hop neighborhood count very little, in terms of rate, in the local entropy conditioning,
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Numerical Simulations
• Source model: multivariate Gaussian random field.
• Correlation model: an exponential model that decays exponentially with the distance between the nodes.
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Numerical Simulations
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Numerical Simulations
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Conclusions
• This paper addressed the problem of joint rate allocation and transmission structure optimization for sensor networks.
• It was shown that – in single-sink case the optimal transmission structure is
the shortest path tree.– in the multiple-sink case the optimization of
transmission structure is NP-complete.• Steiner tree problem