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Berger-Tung inner bound contains the Slepian-Wolf region
Example
For a finite IID -source, take and Hamming distortion at . Choosing in the Berger-Tung test-channel construction makes its subset inequalities the Slepian-Wolf inequalities. This is a lossless special case where the inner bound matches the exact rate region; no such equality is asserted for general multiterminal lossy coding.
Facts & Assumptions
Given: A finite IID -source, identity auxiliary channels , and zero Hamming distortions.
Finite-alphabet Berger-Tung multiterminal lossy inner bound: the finite test-channel subset constraints are for every nonempty .
Finite-IID k-source Slepian–Wolf rate region: the exact finite-IID lossless rate region is for every nonempty .
Finite conditional mutual information identities and equality: for finite variables.
Finite conditional entropy: conditional entropy averages the entropies of the conditional laws over positive-mass conditioning values.
Finite fixed-length multiterminal lossless code: separate finite-length encoders send indices to a common decoder, and achievable rates allow vanishing block-error probability.
Verification
With , every test channel is the identity and the product factorization is satisfied. The decoder sets , so all Hamming distortions vanish. The subset right-hand side in [F1] becomes .
For each positive-mass value of , the conditional law of has mass at and zero off the diagonal. Its entropy therefore equals that of given by [F5]. Averaging over using [F4] gives , so [F3] yields . This also covers , whose complement is a singleton. These are exactly the inequalities in [F2], under the lossless fixed-length common-decoder convention [F6]; lossless block-error achievability is supplied by [F2]. Therefore the Berger-Tung inner bound contains the exact Slepian-Wolf region for this zero-distortion special case, while its statement remains only an inner bound for general distortions.
Depends on
- Finite distributions, entropy, divergence, and stochastic matrices
- Finite conditional entropy
- Finite-alphabet IID multisource
- Finite-alphabet multiterminal lossy source code
- Finite fixed-length multiterminal lossless code
- Finite-alphabet Berger-Tung multiterminal lossy inner bound
- Finite conditional mutual information identities and equality
- Finite-IID k-source Slepian–Wolf rate region
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Abbas El Gamal and Young-Han Kim, Network Information Theory, author lecture notes (standard reference, not scraped)
- João Barros, Reachback Communication in Wireless Sensor Networks, doctoral dissertation (standard reference, not scraped)