Add generic localSettings.yaml as reference

Makefile

@@ -6,6 +6,6 @@ build_folder:
 letter.pdf: letter.tex
 	latexmk -pdf letter.tex
 
-
 clean:
 	rm -rf build
+

README.md

@@ -1,2 +1,28 @@
-# ba-letter
+# ba-letter
-
+
+Repository containing latex source for the Bachelor's Thesis paper.
+
+After cloning, make sure to initialize the submodules containing the dependencies:
+```bash
+$ git submodule update --init
+```
+
+## Build
+
+### Build on host
+
+```bash
+$ make
+```
+
+### Build using docker
+
+1. Build docker image
+    ```bash
+    $ docker build -f dockerfiles/Dockerfile.alpine . -t ba-letter
+    ```
+2. Build examples
+    ```bash
+    $ docker run --rm -v $PWD:$PWD -w $PWD -u `id -u`:`id -g` ba-letter make
+    ```
+

letter.tex

@@ -265,8 +265,8 @@ function \cite{proximal_paper}
 The objective function is minimized using the proximal gradient method, which
 amounts to iteratively performing two gradient-descent steps \cite{proximal_paper}
 with the given objective function and considering AWGN channels.
-To this end, two helper variables, $\boldsymbol{r}$ and $\boldsymbol{s}$, are introduced,
+To this end, two helper variables, $\boldsymbol{r}$ and $\boldsymbol{s}$, are
-describing the result of each of the two steps:
+introduced, describing the result of each of the two steps:
 %
 \begin{alignat}{3}
     \boldsymbol{r} &\leftarrow \boldsymbol{s}
@@ -285,7 +285,8 @@ stages of the decoding process.
 
 As the gradient of the code-constraint polynomial can attain very large values
 in some cases, an additional step is introduced to ensure numerical stability:
-every current estimate $\boldsymbol{s}$ is projected onto $\left[-\eta, \eta\right]^n$ by a projection
+every current estimate $\boldsymbol{s}$ is projected onto
+$\left[-\eta, \eta\right]^n$ by a projection
 $\Pi_\eta : \mathbb{R}^n \rightarrow \left[-\eta, \eta\right]^n$, where $\eta$
 is a positive constant slightly larger than one, e.g., $\eta = 1.5$.
 The resulting decoding process as described in \cite{proximal_paper} is
@@ -578,7 +579,8 @@ oscillate after a certain number of iterations.%
 Considering the magnitude of oscillation of the gradient of the code constraint
 polynomial, some interesting behavior may be observed.
 Figure \ref{fig:p_error} shows the probability that a component of the estimate
-is wrong, determined through a Monte Carlo simulation, when the components of $\boldsymbol{c}$ are ordered from smallest to largest oscillation of
+is wrong, determined through a Monte Carlo simulation, when the components of
+$\boldsymbol{c}$ are ordered from smallest to largest oscillation of
 $\left(\nabla h\right)_i$.
 
 The lower the magnitude of the oscillation, the higher the probability that the
@@ -666,15 +668,15 @@ generated and an ``ML-in-the-list'' step is performed.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Simulation Results \& Discussion}
 
-Figure \ref{fig:results} shows the FER and BER resulting from applying proximal
+Figure \ref{fig:results} shows the FER and BER resulting from applying
-decoding as presented in \cite{proximal_paper} and the improved algorithm
+proximal decoding as presented in \cite{proximal_paper} and the improved
-presented here when applied to a $\left( 3,6 \right)$-regular LDPC code with $n=204$ and
+algorithm presented here when applied to a $\left( 3,6 \right)$-regular LDPC
-$k=102$ \cite[204.33.484]{mackay}.
+code with $n=204$ and $k=102$ \cite[204.33.484]{mackay}.
 The parameters chosen for the simulation are
 $\gamma = 0.05, \omega=0.05, \eta=1.5, K=200$.
 Again, these parameters were chosen,%
 %
-\begin{figure}[H]
+\begin{figure}[ht]
     \centering
 
     \begin{tikzpicture}
@@ -703,7 +705,7 @@ Again, these parameters were chosen,%
             legend columns=2,
             legend style={draw=white!15!black,
                 legend cell align=left,
-                at={(0.5,-0.5)},anchor=south}
+                at={(0.5,-0.44)},anchor=south}
         ]
 
             \addplot+[ProxPlot, scol1]
@@ -772,15 +774,19 @@ Wadayama et al. \cite{proximal_paper} is introduced for AWGN channels.
 It relies on the fact that most errors observed in proximal decoding stem
 from only a few components of the estimate being wrong.
 These few erroneous components can mostly be corrected by appending an
-additional step to the original algorithm that is only executed if the algorithm has not converged.
+additional step to the original algorithm that is only executed if the
+algorithm has not converged.
 A gain of up to $\sim\SI{1}{dB}$ can be observed, depending on the code,
 the parameters considered, and the SNR.
 
-While this work serves to introduce an approach to improve proximal decoding
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-by appending an ``ML-in-the-list'' step, the method used to detect the most
+\section{Acknowledgements}
-probably wrong components of the estimate is based mainly on empirical
+
-observation and a more mathematically rigorous foundation for determining these
+This work has received funding in part from the European Research Council
-components could be beneficial.
+(ERC) under the European Union’s Horizon 2020 research and innovation
+programme (grant agreement No. 101001899) and in part from the German Federal
+Ministry of Education and Research (BMBF) within the project Open6GHub
+(grant agreement 16KISK010).
 
 
 %

localSettings.yaml

@@ -0,0 +1,29 @@
+indentPreamble: 1
+defaultIndent: '    '
+maxNumberOfBackUps: 9
+modifyLineBreaks:
+    preserveBlankLines: 1
+    condenseMultipleBlankLinesInto: 0
+    oneSentencePerLine:
+        manipulateSentences: 1
+        removeSentenceLineBreaks: 0
+        sentencesFollow:
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+            blankLine: 1
+            fullStop: 1
+            exclamationMark: 1
+            questionMark: 1
+            rightBrace: 1
+            commentOnPreviousLine: 1
+            other: 0
+        sentencesBeginWith:
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+            a-z: 0
+            other: 0
+        sentencesEndWith:
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+            exclamationMark: 1
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+            other: '(?:\.\)(?!\h*[a-z]))|(?:(?<!(?:(?:e\.g)|(?:i\.e)|(?:etc))))\.(?!(?:[a-z]|[A-Z]|\-|\,|\.|[0-9]))'
+