13,768 research outputs found
Achieving Marton's Region for Broadcast Channels Using Polar Codes
This paper presents polar coding schemes for the 2-user discrete memoryless
broadcast channel (DM-BC) which achieve Marton's region with both common and
private messages. This is the best achievable rate region known to date, and it
is tight for all classes of 2-user DM-BCs whose capacity regions are known. To
accomplish this task, we first construct polar codes for both the superposition
as well as the binning strategy. By combining these two schemes, we obtain
Marton's region with private messages only. Finally, we show how to handle the
case of common information. The proposed coding schemes possess the usual
advantages of polar codes, i.e., they have low encoding and decoding complexity
and a super-polynomial decay rate of the error probability.
We follow the lead of Goela, Abbe, and Gastpar, who recently introduced polar
codes emulating the superposition and binning schemes. In order to align the
polar indices, for both schemes, their solution involves some degradedness
constraints that are assumed to hold between the auxiliary random variables and
the channel outputs. To remove these constraints, we consider the transmission
of blocks and employ a chaining construction that guarantees the proper
alignment of the polarized indices. The techniques described in this work are
quite general, and they can be adopted to many other multi-terminal scenarios
whenever there polar indices need to be aligned.Comment: 26 pages, 11 figures, accepted to IEEE Trans. Inform. Theory and
presented in part at ISIT'1
Sparse Regression Codes for Multi-terminal Source and Channel Coding
We study a new class of codes for Gaussian multi-terminal source and channel
coding. These codes are designed using the statistical framework of
high-dimensional linear regression and are called Sparse Superposition or
Sparse Regression codes. Codewords are linear combinations of subsets of
columns of a design matrix. These codes were recently introduced by Barron and
Joseph and shown to achieve the channel capacity of AWGN channels with
computationally feasible decoding. They have also recently been shown to
achieve the optimal rate-distortion function for Gaussian sources. In this
paper, we demonstrate how to implement random binning and superposition coding
using sparse regression codes. In particular, with minimum-distance
encoding/decoding it is shown that sparse regression codes attain the optimal
information-theoretic limits for a variety of multi-terminal source and channel
coding problems.Comment: 9 pages, appeared in the Proceedings of the 50th Annual Allerton
Conference on Communication, Control, and Computing - 201
A Comparison of Superposition Coding Schemes
There are two variants of superposition coding schemes. Cover's original
superposition coding scheme has code clouds of the identical shape, while
Bergmans's superposition coding scheme has code clouds of independently
generated shapes. These two schemes yield identical achievable rate regions in
several scenarios, such as the capacity region for degraded broadcast channels.
This paper shows that under the optimal maximum likelihood decoding, these two
superposition coding schemes can result in different rate regions. In
particular, it is shown that for the two-receiver broadcast channel, Cover's
superposition coding scheme can achieve rates strictly larger than Bergmans's
scheme.Comment: 5 pages, 3 figures, 1 table, submitted to IEEE International
Symposium on Information Theory (ISIT 2013
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