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data: add SC-LDPC results and comprehensive comparison plots

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SC-LDPC threshold saturation results:
- SC original staircase: 2.28 photons/slot (vs 4.76 uncoupled)
- SC DE-optimized: 1.03 photons/slot (vs 3.21 uncoupled)
- Shannon limit: 0.47 photons/slot (remaining gap: 3.4 dB)

Add CLI to sc_ldpc.py (threshold, fer-compare, full subcommands).
Add threshold progression, SC threshold bars, and SC FER plots.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
cah
2026-02-24 18:35:50 -07:00
parent 41e2ef72ec
commit b4d5856bf9
6 changed files with 437 additions and 0 deletions
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184
model/plot_de_results.py
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@@ -10,6 +10,7 @@ from pathlib import Path
RESULTS_PATH = Path(__file__).parent.parent / 'data' / 'de_results.json'
RESULTS_Z128_PATH = Path(__file__).parent.parent / 'data' / 'de_results_z128.json'
SC_RESULTS_PATH = Path(__file__).parent.parent / 'data' / 'sc_ldpc_results.json'
OUT_DIR = Path(__file__).parent.parent / 'data' / 'plots'
# Presentation-friendly style
@@ -294,6 +295,180 @@ def plot_z128_comparison(data_z32, data_z128):
print(f' Saved z128_fer_comparison.png')
def load_sc_results():
"""Load SC-LDPC results if available."""
if SC_RESULTS_PATH.exists():
with open(SC_RESULTS_PATH) as f:
return json.load(f)
return None
def plot_threshold_progression(data_z32, data_sc):
"""
Threshold progression: original -> DE-optimized -> normalized -> SC-LDPC,
with Shannon limit line. The key summary plot.
"""
fig, ax = plt.subplots(figsize=(10, 5.5))
shannon_limit = 0.47
# Build progression data
stages = []
values = []
colors_bar = []
# Stage 1: Original staircase (offset)
orig_thresh = data_z32['reference_thresholds'].get(
'Original staircase [7,2,2,2,2,2,2,1]', 5.23)
stages.append('Original\nstaircase\n(offset)')
values.append(float(orig_thresh))
colors_bar.append('#d62728')
# Stage 2: DE-optimized (offset)
opt_thresh = data_z32['best_threshold']
stages.append('DE-optimized\n(offset)')
values.append(float(opt_thresh))
colors_bar.append('#ff7f0e')
# Stage 3: Normalized min-sum
if data_sc and 'uncoupled_thresholds' in data_sc:
norm_thresh = data_sc['uncoupled_thresholds'].get('optimized_normalized', opt_thresh)
stages.append('DE-optimized\n(normalized)')
values.append(float(norm_thresh))
colors_bar.append('#2ca02c')
# Stage 4: SC-LDPC
if data_sc and 'sc_thresholds' in data_sc:
sc_thresh = data_sc['sc_thresholds'].get('sc_optimized',
data_sc['sc_thresholds'].get('sc_original', None))
if sc_thresh is not None:
stages.append('SC-LDPC\n(normalized)')
values.append(float(sc_thresh))
colors_bar.append('#9467bd')
x = np.arange(len(stages))
bars = ax.bar(x, values, color=colors_bar, width=0.6, edgecolor='white', linewidth=1.5)
# Value labels on bars
for bar, val in zip(bars, values):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.08,
f'{val:.2f}', ha='center', va='bottom', fontweight='bold', fontsize=13)
# Shannon limit line
ax.axhline(y=shannon_limit, color='black', linestyle='--', linewidth=2, alpha=0.7)
ax.text(len(stages) - 0.5, shannon_limit + 0.08,
f'Shannon limit ({shannon_limit})',
fontsize=11, ha='right', va='bottom', style='italic', alpha=0.7)
# dB annotations between bars
for i in range(1, len(values)):
if values[i] > 0 and values[i-1] > 0:
gain_db = 10 * np.log10(values[i-1] / values[i])
mid_y = (values[i-1] + values[i]) / 2
ax.annotate(f'{gain_db:.1f} dB',
xy=(i, values[i] + 0.02),
xytext=(i - 0.5, mid_y + 0.3),
fontsize=10, color='#555555',
arrowprops=dict(arrowstyle='->', color='#999999', lw=1),
ha='center')
# Total gap annotation
if len(values) >= 2:
total_gap_db = 10 * np.log10(values[-1] / shannon_limit)
ax.text(len(stages) / 2, max(values) * 0.85,
f'Remaining gap to Shannon: {total_gap_db:.1f} dB',
ha='center', fontsize=12, fontweight='bold',
bbox=dict(boxstyle='round,pad=0.3', facecolor='lightyellow', alpha=0.8))
ax.set_xticks(x)
ax.set_xticklabels(stages, fontsize=11)
ax.set_ylabel(r'DE Threshold ($\lambda_s^*$, photons/slot)')
ax.set_title('Shannon Limit Roadmap: Threshold Progression')
ax.set_ylim(0, max(values) * 1.2)
ax.grid(True, alpha=0.2, axis='y')
fig.savefig(OUT_DIR / 'threshold_progression.png')
plt.close(fig)
print(f' Saved threshold_progression.png')
def plot_sc_threshold_bars(data_sc):
"""SC threshold vs uncoupled threshold bar chart."""
if not data_sc:
return
fig, ax = plt.subplots(figsize=(8, 5))
shannon_limit = data_sc.get('shannon_limit', 0.47)
thresholds = {}
if 'uncoupled_thresholds' in data_sc:
ut = data_sc['uncoupled_thresholds']
thresholds['Original\n(offset)'] = (float(ut['original_offset']), '#d62728')
thresholds['DE-opt\n(offset)'] = (float(ut['optimized_offset']), '#ff7f0e')
thresholds['DE-opt\n(normalized)'] = (float(ut['optimized_normalized']), '#2ca02c')
if 'sc_thresholds' in data_sc:
st = data_sc['sc_thresholds']
thresholds['SC\noriginal'] = (float(st['sc_original']), '#17becf')
thresholds['SC\nDE-opt'] = (float(st['sc_optimized']), '#9467bd')
names = list(thresholds.keys())
vals = [v[0] for v in thresholds.values()]
colors = [v[1] for v in thresholds.values()]
bars = ax.bar(names, vals, color=colors, width=0.55, edgecolor='white', linewidth=1.5)
for bar, val in zip(bars, vals):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05,
f'{val:.2f}', ha='center', va='bottom', fontweight='bold', fontsize=12)
ax.axhline(y=shannon_limit, color='black', linestyle='--', linewidth=1.5, alpha=0.6)
ax.text(len(names) - 0.5, shannon_limit + 0.05,
f'Shannon ({shannon_limit})', fontsize=10, ha='right', style='italic', alpha=0.7)
ax.set_ylabel(r'DE Threshold ($\lambda_s^*$, photons/slot)')
ax.set_title('SC-LDPC Threshold Saturation Effect')
ax.set_ylim(0, max(vals) * 1.25)
ax.grid(True, alpha=0.2, axis='y')
fig.savefig(OUT_DIR / 'sc_threshold_comparison.png')
plt.close(fig)
print(f' Saved sc_threshold_comparison.png')
def plot_sc_fer_comparison(data_sc):
"""FER comparison: SC-LDPC vs uncoupled."""
if not data_sc or 'fer_comparison' not in data_sc:
return
fig, ax = plt.subplots(figsize=(8, 5.5))
fer_data = data_sc['fer_comparison']
lam_s_points = fer_data['lam_s_points']
sc_fer = fer_data['sc_fer']
# SC-LDPC FER
fer_vals = [sc_fer[str(l)]['fer'] for l in lam_s_points]
floor = 5e-3
ax.semilogy(lam_s_points, [max(f, floor) for f in fer_vals],
color='#9467bd', marker='D', markersize=9, linewidth=2.5,
label=f'SC-LDPC (L={fer_data["params"]["L"]}, windowed)',
markeredgecolor='white', markeredgewidth=0.8)
ax.set_xlabel(r'Signal photons/slot ($\lambda_s$)')
ax.set_ylabel('Frame Error Rate (FER)')
L = fer_data['params']['L']
ax.set_title(f'SC-LDPC FER (L={L}, w=2, Z=32, normalized alpha=0.875)')
ax.legend(loc='upper right', framealpha=0.9)
ax.set_xlim(1.5, 10.5)
ax.set_ylim(floor / 2, 1.1)
ax.grid(True, alpha=0.3, which='both')
fig.savefig(OUT_DIR / 'sc_fer.png')
plt.close(fig)
print(f' Saved sc_fer.png')
def main():
OUT_DIR.mkdir(parents=True, exist_ok=True)
data = load_results()
@@ -311,6 +486,15 @@ def main():
else:
print(' Skipping Z=128 comparison (data/de_results_z128.json not found)')
# SC-LDPC plots
data_sc = load_sc_results()
if data_sc is not None:
plot_sc_threshold_bars(data_sc)
plot_sc_fer_comparison(data_sc)
plot_threshold_progression(data, data_sc)
else:
print(' Skipping SC-LDPC plots (data/sc_ldpc_results.json not found)')
print(f'\nAll plots saved to {OUT_DIR}/')