Wall-material fidelity: does the ITU wall palette shift second-order CSI statistics?

Current assessment (2026-09-15)

Eight single-seed simulations show strong sensitivity to a full material-palette swap. They do not quantify real material-tag error or provide independent field replication.

Reported KS distances were 0.74–1.00 for amplitude and 0.27–0.60 for delay spread. The original p-values compare simulated distributions under one geometry/seed; 'highly significant' obscures that replication limit. The session also recorded a failed lint and skipped critic, and no material-KS figure was rendered.

Next evidence needed: Keep the effect as conditional model sensitivity. Test realistic material uncertainty and held-out measured spatial patterns through EXP-F4 before treating the palette as calibrated.

Basis: Thesis Narrative, the September 10 synthesis, and the September 15 register review. See the review record for evidence sources and outstanding checks.

Earlier protocol and dated results

The methods and observations below retain their original scope. Read their readiness statements and proposed next steps as historical. The current assessment above records what has changed; frozen thresholds have not been retrospectively revised.

The problem, in plain words

Think of a room as an echo chamber for radio. When a Wi-Fi signal leaves the router, it does not travel in one clean line — it bounces off walls, floors, and furniture, and the receiver hears a pile-up of echoes arriving at slightly different times. Crowd counting works because a human body is a wet, radio-absorbing obstacle that changes which echoes survive: more people, more disturbance. That is the signal.

But the echoes also depend on what the walls are made of. A concrete wall reflects far more strongly and passes far less through than a sheet of drywall — physically, concrete has both a higher permittivity (roughly ε_r 5.3 vs 2.9) and higher conductivity. So the same people in the same room produce a different radio fingerprint depending on the walls. In a real building you rarely know the wall materials; in our simulator we assign them from a lookup table (ITU-R P.2040). The worry is simple: if our assigned materials are wrong, the fingerprint the sandbox learns is wrong too — and no amount of clever machine learning downstream can undo a mis-modelled room.

There is a wrinkle that reframes the whole question. When we went to set up a "heterogeneous walls vs uniform walls" comparison, we discovered the scene importer does not tag per-wall materials at all — every floor currently stages as uniform drywall. So the honest, testable question is not "heterogeneous vs uniform" but "does the single uniform material we picked actually matter?" — tested by forcing the whole floor to concrete and measuring the shift.

What we are trying to prove

• Hypothesis (falsifiable): swapping the wall material shifts the CSI statistics enough to matter — measurably, on identical geometry and placements. Concretely, KS-D ≥ 0.15 on the delay-spread (second-order) distribution in any band × occupancy cell means material is load-bearing. If instead KS-D stays below 0.15 everywhere, the sim's occupancy signal is robust to this modelling choice and uniform scenes are licensed as a cheap default.
• What a null would have meant: a small KS everywhere would have been the convenient answer — it would let every past and future campaign skip material tagging and stage the cheapest possible scenes. We did not get it.
• The pre-registered decision rule (either way): small KS everywhere → "uniform scenes licensed"; any large KS → "a material-fidelity caveat attaches to every prior default-material CSI result, and the affected sealed sessions are named." This binary was fixed before the run, which is what makes the hit meaningful rather than a fishing expedition.

How the experiment works (plain method)

1. Fix everything except material. Take one real multiroom floor (resplan-12439-floor-0) and one link layout (csi-link-resplan-12439-multiroom). Hold the person placements and the random seed (seed 0) constant. The only thing that changes between the two arms is the wall material: as-stored (uniform drywall) vs forced scene_overrides.uniform_material: concrete.
2. Sweep the two nuisance axes so the finding is not a fluke of one setting: carrier band {2.4 GHz, 5.0 GHz} × occupancy {0 people, 6 people}. That is 2 materials × 2 bands × 2 occupancies = 8 runs.
3. Measure two orders of statistic per run. First-order: the per-placement mean amplitude in dB (mean_amp_db) — "how strong is the link on average." Second-order: the delay spread (delay_spread_ns) — "how smeared-out in time are the echoes," which is exactly the multipath structure material is expected to move.
4. Compare distributions, not means. For each (band, occupancy) cell, run a two-sample Kolmogorov–Smirnov test between the drywall and concrete arms on each statistic. KS-D is the maximum gap between the two cumulative distributions: 0 = identical, 1 = no overlap. Report the KS-D and p in a table and apply the decision rule verbatim.

What we've found so far (honest)

The session (01KT9BJ82A61X7T7HQYY7D9309, /campaign-curious, 2026-06-04) resolved all three success criteria true on 8/8 gate-passed runs, no NaN/Inf. The verdict is unambiguous:

Per-link maxima reach KS-D = 1.00. The decision rule fires on its strongest branch (KS-D ≥ 0.15 on both first and second order): the wall-material palette is load-bearing for both blockage and multipath statistics. Per the pre-registered rule, the caveat attaches to prior default-material CSI sessions — the synthesis names c-csi-crowd-temporal/01KT99FX2Z3RG2W2DZ4FVCVTH7 and c-csi-cross-geometry-resplan/01KT98WKH2HYQYP4EP79G09CEG, plus the unsealed occlusion/body-EM corpora, as having run on the untagged uniform-drywall default.

Three things that keep this honest:

• It is sensitivity, not mis-tagging error. The perturbation is a full drywall→concrete swap — a large, deliberate worst case. It quantifies how far the statistics can move, not how far they do move under a plausible tagging mistake. Drywall is the reasonable material for ResPlan interior partitions, so this makes prior results conditional on the drywall tag being correct, it does not invalidate them.
• The relative cross-geometry result is not confounded. The ×3.8–×7.7 LOFO drift measured in c-csi-cross-geometry-resplan used the same palette on all six floors, so palette uncertainty bounds the absolute magnitudes only — the relative drift comparison stands.
• The session's own hygiene is imperfect. figures: [] — the requested csi_material_ks ECDF small-multiples was never rendered (the KS numbers live only in a material_ks.csv in the session scratch dir); the session sealed with lint.ok = false (a sim_params shape-validation failure); the critic was skipped (supervisor inlined the synthesis under the IP-084 tiny-corpus cost rule); and budget_used logged all zeros (an accounting gap, not a free run). The placement count is also recorded inconsistently across the record — the brief's sim_params says 48, success-criterion 1 says 64, the synthesis says 24 — a provenance smell worth reconciling before this number is quoted anywhere load-bearing.

(No "How to read the figures" section follows, because no figure exists — that is itself the top action item for the next session.)

Key references

• hoydis2023_7aa4 ↗ — the ray tracer that produces the CSI and defines the RadioMaterial abstraction under test.
• zhang2024_df4a ↗ — indoor propagation with segmentation-grounded materials; the fidelity bar the materials/twin voices invoke.
• zhu2024_cbfa ↗ — quantifies how much EM output depends on solver/material modelling choices; grounds the identifiability critique.
• testolina2024_710f ↗ — site-specific digital-twin ray tracing; the provenance model the building-systems voice wants.
• wang2015_48cf ↗ — first-principles CSI-multipath model behind the "material moves the signal" mechanism.
• zou2018_1590 ↗ — real device-free CSI occupancy; the deployment target the fidelity question serves.
• cominelli2023_e6ee ↗ — CSI measurement design & limitations; grounds the statistician's power/multiplicity asks.
• zhang2026_ccac ↗, guarino2026_e72c ↗ — the reproducibility protocols the SWE voice holds this session to.
• gringoli2019_68e7 ↗ — commodity real-hardware CSI extraction; the practitioner's "recalibrate in situ" path.
• huang2025_060d ↗, wang2026_2758 ↗ — the real-CSI anchor and generalizability framing the red-team demands.