# Linear Codes for Distributed Source Coding: Reconstruction of a Function of the Sources D. Krithivasan and S. Sandeep Pradhan

Yüklə 525 b.
 tarix 20.09.2018 ölçüsü 525 b.

• ## Centralized encoder:

• Compute
• Compress using a good source encoder

• ## Are there good source codes with this property?

• Linear Codes.

• ## matrix such that:

• Decoder with high probability.
• Entropy achieving:

• ## Both encoders use identical codebooks

• Binning completely “correlated”
• Independent binning more prevalent in information theory.

• ## General lossless strategy:

• “Embed” the function in a digit plane field (DPF).
• DPF – direct sum of Galois fields of prime order.
• Encode the digits sequentially using Korner-Marton strategy.

• ## Codes used in KM, SW – good channel codes

• Cosets bin the entire space.
• Suitable for lossless coding.
• ## Lossy coding: Need to quantize first.

• Decrease coset density.

• ## Codes used in KM, SW – good channel codes

• Cosets bin the entire space.
• Suitable for lossless coding.
• ## Lossy coding: Need to quantize first.

• Decrease coset density – Nested linear codes.
• Fine code: quantizes the source.
• Coarse code: bins only the fine code.

• ## We need

• : “good” source code
• :“good” channel code
• Can find unique typical for a given

• ## Not a good source code in the Shannon sense.

• Contains a subset that is a good Shannon source code.

• ## Not a good channel code in the Shannon sense.

• Every coset contains a subset which is a good channel code.

• ## Can also recover

• Berger-Tung inner bound.
• Wyner-Ziv rate region.
• Wyner’s source coding with side information.
• Slepian-Wolf and Korner Marton rate regions.

• ## Can achieve more rate points by

• Choosing more general test channels.
• Embedding in

• ## Presents new rate regions for other problems.

Dostları ilə paylaş:

Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©genderi.org 2019
rəhbərliyinə müraciət

Ana səhifə