Add paragraphs exp. params, description, and interpretation for fig. 4.9

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2026-05-02 16:58:02 +02:00
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commit 5687499b5b
2 changed files with 82 additions and 114 deletions

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@@ -1476,23 +1476,6 @@ irrespective of the initialization, can beat, since by construction
whole-block decoding has access to the full set of constraints whole-block decoding has access to the full set of constraints
available to any window. available to any window.
% [Description] Figure 4.9
% - Parameters
% - # BP iterations
% - W,F
% - Physical error rates
% - Warm vs cold start
% - Figure description
% - TODO:
% [Interpretation] Figure 4.9
% -
% At some later point
\content{When looking at max iterations: Callback to diminishing
returns with growing window size: More iterations more beneficial
than larger window (+1 for warm-start)}
\begin{figure}[t] \begin{figure}[t]
\centering \centering
\begin{tikzpicture} \begin{tikzpicture}
@@ -1598,104 +1581,86 @@ than larger window (+1 for warm-start)}
\caption{ \caption{
\red{\lipsum[2]} \red{\lipsum[2]}
} }
\label{fig:bp_w_over_iter}
\end{figure} \end{figure}
% \begin{figure}[t] % [Experimental parameters] Figure 4.9
% \centering
% \begin{tikzpicture}[spy using outlines={circle, magnification=2, \Cref{fig:bp_w_over_iter} shows the per-round \ac{ler} as a function
% connect spies}] of the maximum number of \ac{bp} iterations granted to the inner decoders.
% The dashed colored curves correspond to cold-start sliding-window
% \begin{axis}[ decoding for $W \in \{3, 4, 5\}$, the solid colored curves to the
% width=\figwidth, corresponding warm-start sliding-window decoding, and the black curve
% height=\figheight, to the whole-block reference.
% ymode=log, The physical error rate is fixed at $p = 0.0025$, the step size at
% % xmode=log, $F = 1$, and the iteration budget is swept over
% legend style={ $n_\text{iter} \in \{32, 128, 256, 512, 1024, 2048, 4096\}$.
% cells={anchor=west}, The enlarged plot magnifies the low-iteration regime
% cells={align=left}, $n_\text{iter} \in [32, 512]$.
% },
% enlargelimits=false, % [Description] Figure 4.9
% ymin=1e-3, ymax=1e-1,
% grid=both, All curves decrease monotonically with the iteration budget, but
% legend pos = north east, contrary to our expectation, none of them appears to fully saturate
% xtick={32,512,1024,2048,4096}, within the swept range: even at $n_\text{iter} = 4096$, every curve
% % xtick={0.001,0.0015,...,0.004}, still exhibits a noticeable downward slope.
% xticklabels = At $n_\text{iter} = 32$, the whole-block curve lies below both the
% {$32$,$512$,$1{,}024$,,$2{,}048$,,$3{,}072$,,$4{,}096$}, $W=4$ and $W=5$ sliding-window curves.
% xtick={32, 512, 1024, 1536, 2048, 2560, 3072, 3584, 4096}, At $n_\text{iter} = 128$ the whole-block curve already performs
% xticklabel style={/pgf/number format/fixed}, better than the $W=4$ sliding-window curve
% xticklabel style={/pgf/number format/precision=4}, and at $n_\text{iter} = 512$ the whole-block and warm-start $W = 5$
% % x tick label style={rotate=45, anchor=north east, curves also cross.
% % inner sep=1mm}, From this point onwards, the whole-block decoder lies strictly below
% scaled x ticks=false, all windowed schemes, this difference becoming more pronounced as the
% xlabel={Number of BP iterations}, iteration budget grows further.
% ylabel={Per-round-LER}, Within the magnified plot, the gap between the warm-start and
% extra description/.code={ cold-start curves at fixed $W$ is largest for the smallest iteration
% \node[rotate=90, anchor=south] counts and shrinks rapidly as $n_\text{iter}$ grows, and at fixed
% at ([xshift=10mm]current axis.east) $n_\text{iter}$ the size of this gap grows with the window size,
% {Warm s. (---), Cold s. (- - -)}; mirroring the behavior already observed in \Cref{fig:whole_vs_cold_vs_warm}.
% },
% ] % [Interpretation] Figure 4.9
%
% \foreach \W/\col/\mark in These observations are largely consistent with the effective-iterations
% {3/KITred/triangle,4/KITblue/diamond,5/KITorange/square} { hypothesis put forward above.
% \edef\temp{\noexpand The whole-block decoder eventually overtaking every windowed scheme
% \addplot+[mark=\mark, densely dashed, matches the prediction made there: with a sufficiently large
% forget plot, \col] iteration budget, the whole-block decoder reaches an error rate
% table[ that nonone of the windowed schemes can beat, because of the more global
% col sep=comma, x=max_iter, nature of the considered constraints.
% y=LER_per_round, Furthermore, the pronounced advantage of warm- over cold-start decoding at low
% ] numbers of iterations makes sense if we consider the overall trend of the plots.
% {res/sim/max_iter/WindowingSyndromeMinSumDecoder/p_0.0025/pass_soft_info_False/F_1/W_\W/LERs.csv}; At low iteration budgets, each additional iteration is worth more
% } than at high budgets.
% \temp As the number of permitted iteration increases, the benefit of
% } the additional ``free'' iterations gained due to the the warm-start
% initialization diminishes, and the curves approach each other.
% \foreach \W/\col/\mark in
% {3/KITred/triangle*,4/KITblue/diamond*,5/KITorange/square*} { The fact that no curve clearly saturates within the swept range is
% \edef\temp{\noexpand itself worth noting.
% \addplot+[mark=\mark, solid, mark We know that \ac{bp} on \ac{qldpc} codes suffers from poor
% options={fill=\col}, \col] convergence due to the short cycles in the underlying Tanner graph,
% table[ so even after several thousand iterations the
% col sep=comma, x=max_iter, decoder may continue to slowly refine its message estimates rather
% y=LER_per_round, than settle into a stable fixed point.
% ] This is one of the core motivations for moving from plain \ac{bp} to
% {res/sim/max_iter/WindowingSyndromeMinSumDecoder/p_0.0025/pass_soft_info_True/F_1/W_\W/LERs.csv}; the guided-decimation variant studied in
% } \Cref{subsec:Belief Propagation with Guided Decimation}.
% \temp
% Another thing to note is that setting the per-invocation iteration
% \addlegendentryexpanded{$W = \W$} budget of the inner decoder equal to the iteration budget of the
% } whole-block decoder is not a fair comparison in terms of total
% computational effort.
% \addplot+[mark=*, solid, mark options={fill=black}, black] The sliding-window scheme processes each \ac{vn} in an overlap region
% table[ multiple times and therefore spends more iterations overall.
% col sep=comma, x=max_iter, In the context of \ac{qec}, however, the relevant figure of merit is
% y=LER_per_round, not total compute but decoding latency, and in terms of latency the
% ] sliding-window approach is still at an advantage.
% {res/sim/max_iter/SyndromeMinSumDecoder/p_0.0025/LERs.csv};
% % At some later point
% \addlegendentry{Whole} \content{When looking at max iterations: Callback to diminishing
% returns with growing window size: More iterations more beneficial
% \coordinate (spypoint) at (axis cs:250,1e-2); than larger window (+1 for warm-start)}
% \coordinate (magnifyglass) at (axis cs:2048,0.5);
%
% \end{axis}
% \spy [black, size=4cm] on (spypoint)
% in node[fill=white] at (magnifyglass);
%
% \end{tikzpicture}
%
% \caption{
% Comparison of the decoding performance of cold and warm-start
% decoding under the $\llbracket 144,12,12 \rrbracket$ \ac{bb}
% code for different step sizes.
% Decoding was performed using the min-sum algorithm ($200$
% iterations).
% The window size was fixed to $W=5$, $12$ rounds of syndrome
% extraction were performed and the noise model is
% standard circuit-based depolarizing noise.
% }
% \end{figure}
\begin{figure}[t] \begin{figure}[t]
\centering \centering

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@@ -1,8 +1,11 @@
\chapter{Conclusion and Outlook} \chapter{Conclusion and Outlook}
\content{Takeaway: Warm-start more effective for lower numbers of max
iterations (plays into our hands because lower number of iterations
means lower latency)}
\content{\textbf{Ideas for further research}} \content{\textbf{Ideas for further research}}
\content{Softer way of decimating VNs} \content{Softer way of decimating VNs}
\content{Systematic study on using different inner decoders (AED, \content{Systematic study on using different inner decoders (AED,
SED, BPGD, ...)} SED, BPGD, ...)}
\content{Investigate SC-LDPC window decoding wave-like effects} \content{Investigate SC-LDPC window decoding wave-like effects}