Add windowed vs whole plot
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@@ -238,10 +238,63 @@ decorations.pathreplacing, calc}
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\begin{frame}
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\frametitle{An ``Upper Bound'' on the Performance}
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\begin{itemize}
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\item \red{Whole vs windowed for min sum and spa, different
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window sizes }
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\end{itemize}
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\begin{minipage}{0.65\textwidth}
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\begin{figure}[H]
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% \centering
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\hspace*{-8mm}
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\begin{tikzpicture}
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\begin{axis}[
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width=10cm,
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height=8cm,
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ymode=log,
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ylabel={LER},
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xlabel={Physical error rate},
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legend pos=south east,
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legend style={
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cells={anchor=west},
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cells={align=left},
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},
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xticklabel style={/pgf/number format/fixed},
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xticklabel style={/pgf/number format/precision=4},
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scaled x ticks=false,
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grid,
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xtick={0.001,0.0015,...,0.004},
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xmin=0.001, xmax=0.004,
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]
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\addplot+[NavyBlue, mark=none, line width=1pt]
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table[col sep=comma, x=p, y=LER_sw]
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{res/whole_vs_windowed_spa.csv};
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\addlegendentry{Windowed - SPA}
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\addplot+[BurntOrange, mark=none, line width=1pt]
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table[col sep=comma, x=p, y=LER_sw]
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{res/whole_vs_windowed_min_sum.csv};
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\addlegendentry{Windowed - Min-Sum}
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\addplot+[NavyBlue, mark=none, line width=1pt,
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densely dashed]
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table[col sep=comma, x=p, y=LER_whole]
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{res/whole_vs_windowed_spa.csv};
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\addlegendentry{Whole - SPA}
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\addplot+[BurntOrange, mark=none, line width=1pt,
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densely dashed]
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table[col sep=comma, x=p, y=LER_whole]
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{res/whole_vs_windowed_min_sum.csv};
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\addlegendentry{Whole - Min-Sum}
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\end{axis}
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\end{tikzpicture}
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\end{figure}
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\end{minipage}%
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\begin{minipage}{0.35\textwidth}
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\begin{itemize}
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\item $[[144,12,12]]$ BB code
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\item $n_\text{rounds} = 12$
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\item $W=3$, $F=1$
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\end{itemize}
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\end{minipage}
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\end{frame}
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\begin{frame}
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@@ -272,11 +325,8 @@ decorations.pathreplacing, calc}
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\vspace*{15mm}
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\begin{itemize}
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\item Deeper investigation into difference between hard- and
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soft-info decoding for BP
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\item Look at behavior during iterations
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\item Move from BP to BPGD
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\item Spend much more effort to decode first window (inspired
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by rate-loss of SC-LDPC codes in first window)
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\vspace{15mm}
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@@ -287,5 +337,21 @@ decorations.pathreplacing, calc}
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\end{itemize}
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\end{frame}
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% TODOs
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% - Whole vs windowed for min sum and spa
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% - Basic implementation
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% - Choose parameters (e.g., window sizes)
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% - soft vs hard for min sum and spa (4 plots)
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% - Basic implementation
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% - Choose parameters (e.g., window sizes)
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% - Comparison of numbers of iterations for soft vs hard
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% - Basic implementation (more or less done)
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% - Choose parameters
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% - Convergence analysis
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% - Basic implementation (more or less done)
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% - Choose parameters
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% - Make sure min sum vs spa question makes sense
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% - Think of a few words for each slide (and take notes)
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\end{document}
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@@ -0,0 +1,8 @@
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p,LER_whole,LER_sw
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0.001,0.046,0.086
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0.0015,0.11,0.2
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0.002,0.185,0.345
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0.0025,0.346,0.546
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0.003,0.587,0.721
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0.0035,0.732,0.852
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0.004,0.871,0.941
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@@ -0,0 +1,8 @@
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p,LER_whole,LER_sw
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0.001,0.072,0.101
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0.0015,0.128,0.234
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0.002,0.213,0.352
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0.0025,0.374,0.517
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0.003,0.544,0.676
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0.0035,0.711,0.823
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0.004,0.83,0.905
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