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
+    ```
+

dockerfiles/Dockerfile.alpine

@@ -0,0 +1,7 @@
+FROM alpine:3.19
+
+RUN apk update && apk upgrade
+RUN apk add make texlive texmf-dist-pictures
+RUN apk add texmf-dist-publishers
+RUN apk add texmf-dist-science
+RUN apk add texmf-dist-fontsextra

dockerfiles/Dockerfile.archlinux

@@ -0,0 +1,9 @@
+FROM archlinux:latest
+
+RUN pacman-key --init
+RUN pacman-key --populate archlinux
+RUN pacman -Sy archlinux-keyring --noconfirm && pacman -Su --noconfirm
+
+RUN pacman -Syu --noconfirm
+RUN pacman -S make perl texlive texlive-binextra texlive-pictures --noconfirm
+RUN pacman -S texlive-publishers texlive-mathscience texlive-fontsextra --noconfirm

dockerfiles/Dockerfile.ubuntu

@@ -0,0 +1,7 @@
+FROM ubuntu:22.04
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt update -y && apt upgrade -y
+RUN apt install make texlive latexmk texlive-pictures -y
+RUN apt install make texlive-publishers texlive-science texlive-fonts-extra -y

letter.tex

@@ -1,28 +1,19 @@
 \documentclass[journal]{IEEEtran}
 
+
 \usepackage{amsmath,amsfonts}
+\usepackage{float}
 \usepackage{algorithmic}
 \usepackage{algorithm}
-\usepackage{array}
-\usepackage[caption=false,font=normalsize,labelfont=sf,textfont=sf]{subfig}
-\usepackage{textcomp}
-\usepackage{stfloats}
-\usepackage{url}
-\usepackage{verbatim}
-\usepackage{graphicx}
-\usepackage{cite}
-\usepackage[dvipsnames]{xcolor}
-\usepackage{float}
-\usepackage{tikz}
-\usepackage{pgfplots}
-\usetikzlibrary{spy, arrows.meta,arrows}
-\pgfplotsset{compat=newest}
-\usepgfplotslibrary{colormaps}
-\usepackage{listings}
-\usepackage{lipsum}
-\usepackage{dsfont}
-\usepackage{ulem}
 \usepackage{siunitx}
+\usepackage{dsfont}
+
+\usepackage{tikz}
+\usetikzlibrary{spy, arrows.meta,arrows}
+
+\usepackage{pgfplots}
+\pgfplotsset{compat=newest}
+
 
 \hyphenation{op-tical net-works semi-conduc-tor IEEE-Xplore}
 
@@ -52,6 +43,9 @@
 % TODO
 \pgfplotsset{fancy marks/.style={}}
 
+\newcommand{\figwidth}{\columnwidth}
+\newcommand{\figheight}{0.75\columnwidth}
+
 
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -271,8 +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,
-describing the result of each of the two steps:
+To this end, two helper variables, $\boldsymbol{r}$ and $\boldsymbol{s}$, are
+introduced, describing the result of each of the two steps:
 %
 \begin{alignat}{3}
     \boldsymbol{r} &\leftarrow \boldsymbol{s}
@@ -291,7 +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
@@ -343,52 +338,54 @@ explanation for this effect might be that during the decoding process convergenc
 on the final codeword is often not achieved, although the estimate is moving in
 the right general direction.
 This would suggest that most frame errors occur due to only a few incorrectly
-decoded bits.
-
-\pgfplotsset{
-FERPlot/.style={
-    line width=1pt,
-    densely dashed,
-    mark=triangle,
-	fancy marks
-},
-BERPlot/.style={
-    line width=1pt,
-    mark=*,
-	fancy marks,
-},
-DFRPlot/.style={
-    only marks,
-    mark=square*,
-	fancy marks,
-}}
-
-\begin{figure}[h]
+decoded bits.%
+%
+\begin{figure}[ht]
     \centering
 
+	\pgfplotsset{
+	FERPlot/.style={
+		line width=1pt,
+		densely dashed,
+		mark=triangle,
+		fancy marks
+	},
+	BERPlot/.style={
+		line width=1pt,
+		mark=*,
+		fancy marks,
+	},
+	DFRPlot/.style={
+		only marks,
+		mark=square*,
+		fancy marks,
+	}}
+
     \begin{tikzpicture}
         \begin{axis}[
             grid=both,
             xlabel={$E_\text{b} / N_0$ (dB)}, ylabel={},
             ymode=log,
-            ymax=1.5, ymin=2e-7,
-            width=0.45\textwidth,
-            height=0.3375\textwidth,
+			xmin=1, xmax=8,
+            ymax=1, ymin=1e-6,
+%			ytick={1e-0, 1e-2, 1e-4, 1e-6},
+            width=\figwidth,
+            height=\figheight,
             legend pos = south west,
         ]
-            \addplot+[FERPlot]
+            \addplot+[FERPlot, scol0]
                 table [x=SNR, y=FER, col sep=comma,
                        discard if not={gamma}{0.05},
                        discard if gt={SNR}{9}]
                     {res/proximal_ber_fer_dfr_20433484.csv};
             \addlegendentry{FER}
-            \addplot+[DFRPlot]
+            \addplot+[DFRPlot, scol2]
                 table [x=SNR, y=DFR, col sep=comma,
                        discard if not={gamma}{0.05},
                        discard if gt={SNR}{9}]
                     {res/proximal_ber_fer_dfr_20433484.csv};
             \addlegendentry{DFR}
-            \addplot+[BERPlot]
+            \addplot+[BERPlot, scol1]
                 table [x=SNR, y=BER, col sep=comma,
                        discard if not={gamma}{0.05},
                        discard if gt={SNR}{7.5}]
@@ -434,8 +431,8 @@ optimization process.%
                      xlabel={$\tilde{x}_1$},
                      ylabel={$\tilde{x}_2$},
                      y label style={at={(axis description cs:-0.06,0.5)},anchor=south},
-                     width=0.45\textwidth,
-                     height=0.3375\textwidth,
+			         width=\figwidth,
+					 height=\figheight,
                      grid=major, grid style={dotted},
                      view={0}{90}]
             \addplot3[point meta=\thisrow{grad_norm},
@@ -456,8 +453,8 @@ optimization process.%
                         table[col sep=comma] {res/2d_grad_L.csv};
         \end{axis}
         \begin{axis}[hide axis,
-                     width=0.45\textwidth,
-                     height=0.3375\textwidth,
+			         width=\figwidth,
+					 height=\figheight,
                      xmin=10, xmax=50,
                      ymin=0, ymax=0.4,
                      legend style={draw=white!15!black,
@@ -477,8 +474,8 @@ optimization process.%
     \begin{tikzpicture}
         \begin{axis}[xmin = -1.25, xmax=1.25,
                      ymin = -1.25, ymax=1.25,
-                     width=0.45\textwidth,
-                     height=0.3375\textwidth,
+			         width=\figwidth,
+					 height=\figheight,
                      xlabel={$\tilde{x}_1$},
                      ylabel={$\tilde{x}_2$},
                      y label style={at={(axis description cs:-0.06,0.5)},anchor=south},
@@ -502,8 +499,8 @@ optimization process.%
                         table[col sep=comma] {res/2d_grad_h.csv};
         \end{axis}
         \begin{axis}[hide axis,
-                     width=0.45\textwidth,
-                     height=0.3375\textwidth,
+			         width=\figwidth,
+					 height=\figheight,
                      xmin=10, xmax=50,
                      ymin=0, ymax=0.4,
                      legend style={draw=white!15!black,
@@ -531,8 +528,8 @@ Two qualities may be observed.
 First, the average values of the two gradients are equal, except for their sign,
 leading to the aforementioned oscillation.
 Second, the gradient of the code constraint polynomial itself starts to
-oscillate after a certain number of iterations.
-
+oscillate after a certain number of iterations.%
+%
 \begin{figure}[ht]
     \centering
 
@@ -540,10 +537,13 @@ oscillate after a certain number of iterations.
         \begin{axis}[
             grid=both,
             xlabel={Iterations},
-            width=0.45\textwidth,
-            height=0.3375\textwidth,
+            width=\figwidth,
+            height=\figheight,
             xtick={0, 100, ..., 400},
             xticklabels={0, 50, ..., 200},
+			xmin=0, xmax=300,
+			ymin=-4, ymax=2,
+			ytick={-4,-3,...,2},
             legend pos = south east,
         ]
             \addplot+ [mark=none, line width=1]
@@ -579,7 +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
@@ -596,8 +597,10 @@ the probability that a given component was decoded incorrectly.%
             ylabel=$P(\hat{c}_{i'} \ne c_{i'})$,
             xlabel=$i'$,
             ymode=log,
-            width=0.45\textwidth,
-            height=0.3375\textwidth,
+			ymin=1e-9,ymax=1e-5,
+			xmin=0,xmax=200,
+			width=\figwidth,
+			height=\figheight,
         ]
             \addplot+ [scol1, mark=none, line width=1]
                 table [col sep=comma, y=p_error]{res/p_error.csv};
@@ -665,15 +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
-decoding as presented in \cite{proximal_paper} and the improved algorithm
-presented here when applied to a $\left( 3,6 \right)$-regular LDPC code with $n=204$ and
-$k=102$ \cite[204.33.484]{mackay}.
+Figure \ref{fig:results} shows the FER and BER resulting from applying
+proximal decoding as presented in \cite{proximal_paper} and the improved
+algorithm presented here when applied to a $\left( 3,6 \right)$-regular LDPC
+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}
@@ -695,13 +698,14 @@ Again, these parameters were chosen,%
             grid=both,
             xlabel={$E_\text{b} / N_0$ (dB)},
             ymode=log,
-            ymax=1.5, ymin=2e-7,
-            width=0.45\textwidth,
-            height=0.3375\textwidth,
+			xmin=1, xmax=8,
+            ymax=1, ymin=1e-6,
+			width=\figwidth,
+			height=\figheight,
             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]
@@ -770,15 +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
-probably wrong components of the estimate is based mainly on empirical
-observation and a more mathematically rigorous foundation for determining these
-components could be beneficial.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Acknowledgements}
+
+This work has received funding in part from the European Research Council
+(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:
+            par: 1
+            blankLine: 1
+            fullStop: 1
+            exclamationMark: 1
+            questionMark: 1
+            rightBrace: 1
+            commentOnPreviousLine: 1
+            other: 0
+        sentencesBeginWith:
+            A-Z: 1
+            a-z: 0
+            other: 0
+        sentencesEndWith:
+            basicFullStop: 0
+            betterFullStop: 1
+            exclamationMark: 1
+            questionMark: 1
+            other: '(?:\.\)(?!\h*[a-z]))|(?:(?<!(?:(?:e\.g)|(?:i\.e)|(?:etc))))\.(?!(?:[a-z]|[A-Z]|\-|\,|\.|[0-9]))'
+