Add windowed vs whole plot

This commit is contained in:
2026-03-27 11:20:50 +01:00
parent 733577fbfb
commit 631eeed5cd
3 changed files with 90 additions and 8 deletions

View File

@@ -238,10 +238,63 @@ decorations.pathreplacing, calc}
\begin{frame}
\frametitle{An ``Upper Bound'' on the Performance}
\begin{itemize}
\item \red{Whole vs windowed for min sum and spa, different
window sizes }
\end{itemize}
\begin{minipage}{0.65\textwidth}
\begin{figure}[H]
% \centering
\hspace*{-8mm}
\begin{tikzpicture}
\begin{axis}[
width=10cm,
height=8cm,
ymode=log,
ylabel={LER},
xlabel={Physical error rate},
legend pos=south east,
legend style={
cells={anchor=west},
cells={align=left},
},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
grid,
xtick={0.001,0.0015,...,0.004},
xmin=0.001, xmax=0.004,
]
\addplot+[NavyBlue, mark=none, line width=1pt]
table[col sep=comma, x=p, y=LER_sw]
{res/whole_vs_windowed_spa.csv};
\addlegendentry{Windowed - SPA}
\addplot+[BurntOrange, mark=none, line width=1pt]
table[col sep=comma, x=p, y=LER_sw]
{res/whole_vs_windowed_min_sum.csv};
\addlegendentry{Windowed - Min-Sum}
\addplot+[NavyBlue, mark=none, line width=1pt,
densely dashed]
table[col sep=comma, x=p, y=LER_whole]
{res/whole_vs_windowed_spa.csv};
\addlegendentry{Whole - SPA}
\addplot+[BurntOrange, mark=none, line width=1pt,
densely dashed]
table[col sep=comma, x=p, y=LER_whole]
{res/whole_vs_windowed_min_sum.csv};
\addlegendentry{Whole - Min-Sum}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.35\textwidth}
\begin{itemize}
\item $[[144,12,12]]$ BB code
\item $n_\text{rounds} = 12$
\item $W=3$, $F=1$
\end{itemize}
\end{minipage}
\end{frame}
\begin{frame}
@@ -272,11 +325,8 @@ decorations.pathreplacing, calc}
\vspace*{15mm}
\begin{itemize}
\item Deeper investigation into difference between hard- and
soft-info decoding for BP
\item Look at behavior during iterations
\item Move from BP to BPGD
\item Spend much more effort to decode first window (inspired
by rate-loss of SC-LDPC codes in first window)
\vspace{15mm}
@@ -287,5 +337,21 @@ decorations.pathreplacing, calc}
\end{itemize}
\end{frame}
% TODOs
% - Whole vs windowed for min sum and spa
% - Basic implementation
% - Choose parameters (e.g., window sizes)
% - soft vs hard for min sum and spa (4 plots)
% - Basic implementation
% - Choose parameters (e.g., window sizes)
% - Comparison of numbers of iterations for soft vs hard
% - Basic implementation (more or less done)
% - Choose parameters
% - Convergence analysis
% - Basic implementation (more or less done)
% - Choose parameters
% - Make sure min sum vs spa question makes sense
% - Think of a few words for each slide (and take notes)
\end{document}

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@@ -0,0 +1,8 @@
p,LER_whole,LER_sw
0.001,0.046,0.086
0.0015,0.11,0.2
0.002,0.185,0.345
0.0025,0.346,0.546
0.003,0.587,0.721
0.0035,0.732,0.852
0.004,0.871,0.941
1 p LER_whole LER_sw
2 0.001 0.046 0.086
3 0.0015 0.11 0.2
4 0.002 0.185 0.345
5 0.0025 0.346 0.546
6 0.003 0.587 0.721
7 0.0035 0.732 0.852
8 0.004 0.871 0.941

View File

@@ -0,0 +1,8 @@
p,LER_whole,LER_sw
0.001,0.072,0.101
0.0015,0.128,0.234
0.002,0.213,0.352
0.0025,0.374,0.517
0.003,0.544,0.676
0.0035,0.711,0.823
0.004,0.83,0.905
1 p LER_whole LER_sw
2 0.001 0.072 0.101
3 0.0015 0.128 0.234
4 0.002 0.213 0.352
5 0.0025 0.374 0.517
6 0.003 0.544 0.676
7 0.0035 0.711 0.823
8 0.004 0.83 0.905