Correct abstract
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letter.tex
25
letter.tex
@ -32,8 +32,8 @@
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\ifoverleaf
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\input{common.tex}
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\else
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% \usetikzlibrary{external}
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% \tikzexternalize[prefix=build/]
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\usetikzlibrary{external}
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\tikzexternalize[prefix=build/]
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\input{lib/latex-common/common.tex}
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\fi
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@ -91,20 +91,19 @@ Hertzstr. 16, 76187 Karlsruhe, Germany, Email: \texttt{\{first.last\}@kit.edu}}
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\begin{abstract}
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In this paper, the proximal decoding algorithm is considered within the
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context of \textit{additive white Gaussian noise} (AWGN) channels.
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An analysis of the convergence behavior of the algorithm shows that it is an
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inherent property of proximal decoding to enter an
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oscillating behavior of the estimate after a number of iterations.
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An analysis of the convergence behavior of the algorithm shows that
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proximal decoding inherently enters an oscillating behavior of the estimate
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after a certain number of iterations.
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Due to this oscillation, frame errors arising during decoding can often
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be attributed to only a few remaining wrongly decoded components.
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In this paper, an improvement of the algorithm is proposed by appending an
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additional step, in which these erroneous components are attempted to be
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corrected.
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An empirical rule is suggested, with which the components most likely needing
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be attributed to only a few remaining wrongly decoded bits.
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In this letter, an improvement of the proximal decoding algorithm is proposed
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by appending an additional step, in which these erroneous components are
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attempted to be corrected.
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We suggesst an empirical rule with which the components most likely needing
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correction can be determined.
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Using this insight and performing a subsequent ``ML-in-the-list'' decoding,
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a gain of up to approximately 1 dB is achieved compared to proximal decoding,
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depending on the parameters chosen and the code considered.
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a gain of up to approximately 1 dB is achieved compared to conventional
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proximal decoding, depending on the decoder parameters and the code.
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\end{abstract}
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\begin{IEEEkeywords}
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