Structured key family as the main configuration

Unconstrained key training converged to disjoint sparse supports: 99
percent of each users key energy sat on three or four of the sixteen
entries, with pairwise disjoint supports and one numerically dead
codebook column. That is an orthogonal slot allocation, so the
superposition collapsed into OMA and the key space was far smaller than
the dense direction the brute-force study assumes.

The main configuration is now the structured Walsh-Hadamard family,
which is dense, exactly orthogonal, unit modulus, and already the best
family in the key-family table. base_keys generalizes to any key length
by truncating the next power-of-two Sylvester order, and the key-length
sweep keeps only lengths where the truncated rows stay exactly
orthogonal, verified numerically.

Also fixes the M-PAM energy normalization in oma_ser_keylen, which used
sqrt(6g/(M^2-1)) where unit average symbol energy gives A^2=3/(M^2-1);
the closed form was 3 dB optimistic and now reproduces a direct Monte
Carlo to 1e-5.

Results move accordingly: the proposal now stays below OMA at every SNR
and reaches 1.52x at key length 64, while the jamming margin falls to
5.5-6.3 dB and the brute-force curve to 0.59 at a million guesses.
This commit is contained in:
KiHoLee
2026-08-17 20:02:27 +09:00
parent b120364e38
commit 3d5a7fc6f3
27 changed files with 561 additions and 418 deletions
+73 -26
View File
@@ -1,14 +1,17 @@
# -*- coding: utf-8 -*-
"""Final consistency check: every headline number vs its raw CSV."""
"""Final consistency check: every headline number vs its raw CSV.
A quoted value that goes stale during a revision is the failure mode this
guards against, so each assertion recomputes from data/ rather than from
another quoted value. The manuscript-side assertions are skipped when
main.tex is absent, which is the case in the reproducibility package.
"""
import csv
import math
import re
from pathlib import Path
base = Path(__file__).resolve().parents[1]
# The manuscript is not part of the reproducibility package, so the
# tex-side assertions are skipped when it is absent and the data-side
# assertions still run.
_tex_path = base / "main.tex"
HAVE_TEX = _tex_path.exists()
tex = _tex_path.read_text(encoding="utf-8") if HAVE_TEX else ""
@@ -38,56 +41,100 @@ def chk(label, cond, detail, needs_tex=False):
print("headline numbers vs raw data")
# 1.27x key-length ratio
# --- Fig. 2: the proposal is below OMA -------------------------------
sn = rows("sec_snr.csv")
lg = [float(x["legit"]) for x in sn]
om = [float(x["oma"]) for x in sn]
rel = [(a - b) / b * 100 for a, b in zip(lg, om)]
chk("legit below OMA at every SNR", max(rel) < 0, "max relative %+.2f%%" % max(rel))
chk("gain 1.3 to 7.9 percent",
round(-max(rel), 1) == 1.3 and round(-min(rel), 1) == 7.9,
"%.2f to %.2f percent" % (-max(rel), -min(rel)))
chk("1.3 and 7.9 in tex", "$1.3$ to\n$7.9$~percent" in tex or "$1.3$ to $7.9$~percent" in tex,
"searched tex", needs_tex=True)
ew = [float(x["eve_wrong"]) for x in sn]
ch = float(sn[0]["chance"])
chk("outsider at chance to 2e-5", max(abs(x - ch) for x in ew) < 2e-5,
"max deviation %.2e" % max(abs(x - ch) for x in ew))
# --- Fig. 3: key-length ratio ----------------------------------------
k = rows("sec_keylen.csv")
r64 = [x for x in k if int(x["L"]) == 64][0]
ratio = float(r64["oma"]) / float(r64["legit_ser"])
chk("key-length ratio 1.27", round(ratio, 2) == 1.27, "%.4f" % ratio)
chk("1.27 in tex", tex.count("1.27") >= 2, "%d occurrences" % tex.count("1.27"), needs_tex=True)
chk("key-length ratio 1.52", round(ratio, 2) == 1.52, "%.4f" % ratio)
chk("1.52 in tex", tex.count("1.52") >= 2, "%d occurrences" % tex.count("1.52"),
needs_tex=True)
chk("keys exactly orthogonal in the sweep",
max(float(x["mask_xcorr"]) for x in k) < 1e-6,
"max xcorr %.2e" % max(float(x["mask_xcorr"]) for x in k))
# blind-jammer gap
# --- Fig. 4: jamming --------------------------------------------------
g = col("sec_jam_gap.csv", "gap_db")
chk("gap 7.4-8.1 dB", round(min(g), 1) == 7.4 and round(max(g), 1) == 8.1,
chk("gap 5.5-6.3 dB", round(min(g), 1) == 5.5 and round(max(g), 1) == 6.3,
"%.3f to %.3f" % (min(g), max(g)))
chk("no stale 8.5 dB", "8.5$~dB" not in tex, "searched tex", needs_tex=True)
lin = (10 ** (min(g) / 10), 10 ** (max(g) / 10))
chk("about six times power", lin[0] < 6.5 and lin[1] > 5.5,
chk("about four times power", lin[0] < 4.5 and lin[1] > 3.4,
"%.2f to %.2f" % lin)
# blind vs permutation
j = rows("sec_jam_cmp.csv")
dmax = max(abs(float(r["blind"]) - float(r["perm_blind"])) for r in j)
chk("within 0.002", dmax <= 0.002, "%.5f" % dmax)
chk("no stale 0.0015 in jamming", "$0.0015$ of the proposed" not in tex, "ok", needs_tex=True)
chk("no stale 8.1 dB", "$8.1$~dB" not in tex, "searched tex", needs_tex=True)
# brute force
# --- Fig. 6: brute force ---------------------------------------------
b = rows("sec_brute_cmp.csv")
sm = float(b[-1]["ser_mask"])
chk("brute 0.76 both places", tex.count("$0.76$") >= 2, "%.4f measured" % sm, needs_tex=True)
chk("no stale 0.75 in summary",
"$0.75$ after $10^{6}$" not in tex, "summary row", needs_tex=True)
chk("brute 0.59 at 1e6", round(sm, 2) == 0.59, "%.4f" % sm)
chk("0.59 in tex", "$0.59$" in tex, "searched tex", needs_tex=True)
pad0 = next((x["K"] for x in b if float(x["ser_pad"]) < 0.27), None)
chk("index cipher collapses at 65536", pad0 == "65536", str(pad0))
# refresh
rs = {r["scheme"]: r for r in rows("refresh_summary.csv")}
# --- Fig. 7: known plaintext -----------------------------------------
kp = rows("kpa.csv")
legit = float([x for x in k if int(x["L"]) == 16][0]["legit_ser"])
thr = legit * 1.02
first20 = next((x["n_frames"] for x in kp
if int(x["snr_db"]) == 20 and float(x["eve_ser"]) <= thr), None)
first10 = next((x["n_frames"] for x in kp
if int(x["snr_db"]) == 10 and float(x["eve_ser"]) <= thr), None)
chk("KPA five frames at 20 dB", first20 == "5", "first N = %s" % first20)
chk("KPA twenty-four frames at 10 dB", first10 == "24", "first N = %s" % first10)
pk = rows("pkpa.csv")
p6 = float([x for x in pk if x["n_frames"] == "6"][0]["eve_ser"])
chk("perm KPA at N=6 near its own 0.258", abs(p6 - 0.258) < 0.005, "%.4f" % p6)
# --- refresh ----------------------------------------------------------
rs = {x["scheme"]: x for x in rows("refresh_summary.csv")}
chk("refresh 64.8 bits",
round(float(rs["Invariant"]["entropy_bits"]), 1) == 64.8,
"%.3f" % float(rs["Invariant"]["entropy_bits"]))
chk("fixed key 15.0 bits",
round(float(rs["None (fixed key)"]["entropy_bits"]), 1) == 15.0,
"%.4f" % float(rs["None (fixed key)"]["entropy_bits"]))
chk("invariant refresh free",
abs(float(rs["Invariant"]["legit"]) - float(rs["None (fixed key)"]["legit"]))
< 0.001, "%.4f vs %.4f" % (float(rs["Invariant"]["legit"]),
float(rs["None (fixed key)"]["legit"])))
# permutation KPA
pk = rows("pkpa.csv")
p6 = float([r for r in pk if r["n_frames"] == "6"][0]["eve_ser"])
chk("perm KPA at N=6 near 0.303", abs(p6 - 0.303) < 0.005, "%.4f" % p6)
# --- real tokens ------------------------------------------------------
import json
st = json.loads((base / "data" / "real_sec_stats.json").read_text())
rec = st["recovery"]["28"]
chk("headline recovery 78 vs 76 percent",
round(rec["legit"] * 100) == 78 and round(rec["oma"] * 100) == 76,
"%.1f vs %.1f" % (rec["legit"] * 100, rec["oma"] * 100))
chk("legit leads OMA at every point",
all(st["recovery"][s]["legit"] > st["recovery"][s]["oma"]
for s in st["recovery"]),
"checked %d points" % len(st["recovery"]))
# abstract
# --- abstract ---------------------------------------------------------
a = (tex.split(r"\begin{abstract}")[1].split(r"\end{abstract}")[0].strip()
if HAVE_TEX else "")
w = len(re.split(r"\s+", a)) if a else 0
chk("abstract <= 250 words", w <= 250, "%d words" % w, needs_tex=True)
chk("abstract has no abbreviations",
not re.findall(r"\b[A-Z]{2,}\b", a), str(re.findall(r"\b[A-Z]{2,}\b", a)), needs_tex=True)
not re.findall(r"\b[A-Z]{2,}\b", a), str(re.findall(r"\b[A-Z]{2,}\b", a)),
needs_tex=True)
print()
print("ALL CONSISTENT" if ok else "INCONSISTENCIES FOUND")