Put paper and own simulation results into same plot
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latex/presentations/midterm/res/ber_paper.csv
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11
latex/presentations/midterm/res/ber_paper.csv
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@ -0,0 +1,11 @@
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SNR,gamma_0_15,gamma_0_05,gamma_0_01,BP
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1,0.3669767287,0.0983527875,0.09440942764,0.06443019397
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1.5,0.3669767287,0.07297050292,0.07602178383,0.03398665526
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2,0.3669767287,0.04854153852,0.05563899358,0.01257341759
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2.5,0.3669767287,0.03099619799,0.03962553082,0.003089064275
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3,0.3669767287,0.01635036356,0.02635975022,0.001038700586
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3.5,0.3669767287,0.007422688173,0.01572148666,0.0001272453028
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4,0.3669767287,0.002980400475,0.008639663866,0
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4.5,0.3669767287,0.001044021313,0.004434697304,0
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5,0.3669767287,0.0003969287889,0.002126155473,0
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5.5,0.3908851459,0.00008863357225,0.0007759378638,0
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@ -9,63 +9,84 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{frame}[t]
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\frametitle{Proximal Decoding: Bit Error Rate and Performance}
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\vspace*{-0.5cm}
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\begin{itemize}
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\item Comparison of simulation
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\footnote{(3,6) regular LDPC code with $n=204, k=102$
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\cite[\text{204.33.484}]{mackay_enc}}
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with results of Wadayama and Takabe
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\end{itemize}
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\begin{figure}[H]
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\centering
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\begin{figure}[H]
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\centering
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\begin{subfigure}{0.5\textwidth}
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\centering
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\begin{tikzpicture}[scale=0.5]
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\begin{axis}[
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grid=both, grid style={line width=.1pt},
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xlabel={$E_b / N_0$ (dB)}, ylabel={Bit Error Rate},
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ymode=log,
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% legend style={at={(0.05,0.05)},anchor=south west},
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legend pos=outer north east,
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width=11.5cm,
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height=8cm,
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ymax=1.2, ymin=0.8e-4,
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xtick={1, 2, ..., 5},
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xmin=0.9, xmax=5.6,
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legend columns=2,
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]
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%\addlegendimage{ForestGreen, mark=*, solid}
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%\addlegendentry{proximal, $\gamma = 0.15$}
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%\addlegendimage{NavyBlue, mark=*, solid}
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%\addlegendentry{proximal, $\gamma = 0.01$}
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%
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%\addlegendimage{RedOrange, mark=*, solid}
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%\addlegendentry{proximal, $\gamma = 0.05$}
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%
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%\addlegendimage{Emerald, mark=triangle, densely dashed}
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%\addlegendentry{hybrid, $\gamma = 0.15$}
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%
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%\addlegendimage{RoyalPurple, mark=triangle, densely dashed}
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%\addlegendentry{hybrid, $\gamma = 0.01$}
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%
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%\addlegendimage{red, mark=triangle, densely dashed}
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%\addlegendentry{hybrid, $\gamma = 0.05$}
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\addplot [ForestGreen, mark=triangle, dashed, line width=1pt] table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.15},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.15$}
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\addplot [ForestGreen, mark=*, line width=1pt] table [x=SNR, y=gamma_0_15, col sep=comma] {res/ber_paper.csv};
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\addlegendentry{$\gamma = 0.15$ (Wadayama et al.)}
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\addplot [NavyBlue, mark=triangle, dashed, line width=1pt] table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.01},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.01$}
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\addplot [NavyBlue, mark=*, line width=1pt] table [x=SNR, y=gamma_0_01, col sep=comma] {res/ber_paper.csv};
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\addlegendentry{$\gamma = 0.01$ (Wadayama et al.)}
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\addplot [RedOrange, mark=triangle, dashed, line width=1pt] table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.05},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.05$}
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\addplot [RedOrange, mark=*, line width=1pt] table [x=SNR, y=gamma_0_05, col sep=comma] {res/ber_paper.csv};
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\addlegendentry{$\gamma = 0.05$ (Wadayama et al.)}
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\begin{tikzpicture}[scale=0.45]
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\begin{axis}[
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grid=both,
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xlabel={$E_b / N_0$ (dB)}, ylabel={Bit Error Rate},
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ymode=log,
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legend style={at={(0.05,0.05)},anchor=south west},
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width=11.5cm,
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height=8cm,
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ytick={0, 1e-1, 1e-2, 1e-3, 1e-4},
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xtick={1, 2, 3, 4, 5},
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ymax=1.2, ymin=0.8e-4,
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xmin=0.9, xmax=5.6,
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]
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\addplot table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.15},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.15$}
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\addplot table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.01},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.01$}
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\addplot table [x=SNR, y=BER, col sep=comma,
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discard if not={gamma}{0.05},
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discard if gt={SNR}{5.5},]
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{res/proximal/2d_ber_fer_dfr_20433484.csv};
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\addlegendentry{$\gamma = 0.05$}
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\end{axis}
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\end{tikzpicture}
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\caption{Simulation results for $\omega = 0.05, K=100$}
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\label{fig:sim_results_prox}
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\end{subfigure}%
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\begin{subfigure}{0.5\textwidth}
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\centering
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\addplot [RoyalPurple, mark=*, line width=1pt] table [x=SNR, y=BP, col sep=comma] {res/ber_paper.csv};
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\addlegendentry{BP}
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\includegraphics[scale=0.6]{res/ber_paper}
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\caption{Results from ``Proximal Decoding for LDPC Codes''}
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\label{fig:paper_results_prox}
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\end{subfigure}%
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\end{figure}
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\end{axis}
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\end{tikzpicture}
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\caption{Simulation results for $\omega = 0.05, K=100$}
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\label{fig:sim_results_prox}
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\end{figure}
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\vspace*{-0.5cm}
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\begin{itemize}
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\item $\mathcal{O}\left(n \right) $ time complexity - same as BP;
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only multiplication and addition necessary \cite{proximal_paper}
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\item Measured Performance: $\sim\SI{10000}{}$ frames/s
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@ -81,7 +102,7 @@
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\setcounter{footnote}{0}
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\begin{itemize}
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\item Analysis of simulation
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\item Simulation
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\footnote{(3,6) regular LDPC code with $n=204, k=102$
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\cite[\text{204.33.484}]{mackay_enc}}
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results for different values of $\gamma$
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@ -1438,7 +1459,7 @@ $\textcolor{KITblue}{\text{Output }\boldsymbol{\tilde{c}}_n\text{ with lowest }d
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\begin{tikzpicture}[scale=0.6]
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\begin{axis}[
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grid=both,
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xlabel={k},
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xlabel={Iterations},
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ylabel={Average $\left| \boldsymbol{x}-\boldsymbol{\hat{x}} \right|$},
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legend pos=outer north east,
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]
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