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      Achieving Marton's Region for Broadcast Channels Using Polar Codes

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          Abstract

          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 \(k\) 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.

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          Author and article information

          Journal
          2014-01-23
          2014-10-10
          Article
          1401.6060
          aa43984e-9ccc-431b-aaf3-d76e283337b0

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          26 pages, 11 figures, accepted to IEEE Trans. Inform. Theory and presented in part at ISIT'14
          cs.IT math.IT

          Numerical methods,Information systems & theory
          Numerical methods, Information systems & theory

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