wifi-densepose/docs/research/sota-2026-05-22/R5-subcarrier-saliency.md

R5 — Subcarrier saliency: which CSI dimensions actually carry the signal?

Status: in-flight · Started: 2026-05-21

Motivation

cog-pose-estimation (Conv1d 56 → 64 → 128 → 128) and cog-person-count (same backbone, different heads) both consume 56-subcarrier × 20-frame CSI windows. The 56 came from the upstream align-ground-truth.js aggregation choice, not from a measurement of which subcarriers actually carry the per-task signal. If we could rank subcarriers by their first-order influence on the trained model's output, three concrete wins follow:

1. Smaller-K models for chips with severe CSI bandwidth caps (some ESP32-C5/C6 firmware only exposes 32 subcarriers).
2. Better data collection — focus channel-hopping on the most-informative subcarriers.
3. Adversarial-defence — if an attacker spoofs all 56 subcarriers uniformly, the model still trusts them; a saliency-weighted consistency check spots inconsistent perturbations.

This thread starts with the first item: measure per-subcarrier first-order influence on the v0.0.2 count model + the v0.0.1 pose model, then ask whether top-K subsets of K∈{8,16,32} retain meaningful accuracy.

Method (single-tick scope)

For each model:

1. Load the trained safetensors (cog/artifacts/count_v1.safetensors and cog/artifacts/pose_v1.safetensors).
2. Run forward pass on the 1,077-sample paired dataset (or a stratified 256-sample subset for speed).
3. Compute per-subcarrier gradient × input saliency: S_k = mean_over_samples( |∂loss/∂x_k| · |x_k| ) for each subcarrier k. This is the standard "input × gradient" saliency from Sundararajan et al. (Integrated Gradients) but without the path integral — faster, decent first-order approximation.
4. Plot the 56-element saliency vector for each model. Identify top-K.
5. Re-train each model on the top-K subcarriers only (K ∈ {8, 16, 32}). Compare accuracy.

If time runs out mid-tick, ship steps 1-4 as a first artifact and queue 5 for a later tick. Steps 1-4 alone produce a real result (a ranked-subcarrier list per task).

Why this is novel

ADR-097 mentions "subcarrier attention" abstractly; nothing measured. Published SOTA on WiFi CSI typically uses all available subcarriers — the bandwidth-cap argument is operationally important but academically under-explored. A per-task saliency map is a direct artefact that can be checked against any future architecture choice.

Connections

• Feeds R7 (adversarial multi-link consistency) — top-K subcarriers are the ones a defender most needs to corroborate.
• Feeds R8 (RSSI-only) — if even the top-K subcarriers carry most of the signal, RSSI's information ceiling is sharply lower than full CSI's, putting hard bounds on R8's achievable accuracy.

What gets written

This tick's deliverable is:

• The Python script examples/research-sota/r5_subcarrier_saliency.py that computes the saliency vector for either model.
• A first measurement (text + JSON) of saliency for the count model.

Step 5 (retrain on top-K) is queued for a subsequent tick.

First measurement — cog-person-count v0.0.2 (this tick, 128 samples)

Rank	Subcarrier	Saliency
1	41	0.0128
2	52	0.0120
3	30	0.0100
4	31	0.0097
5	10	0.0088
6	35	0.0088
7	2	0.0087
8	38	0.0083

Max-to-mean ratio: 2.85× — meaningful but moderate concentration. Important secondary observation: top-8 subcarriers are spread across the entire band (indices 2, 10, 30, 31, 35, 38, 41, 52 — not clustered in one frequency region).

Implications

1. Bandwidth-cap deployment is viable. Even at K=8 we retain the highest-saliency subcarriers across the full band — meaning a 32-subcarrier ESP32-C6/C5 build should retain most of the count-task signal. Retraining at K=8/16/32 is the next-tick experiment.
2. R8 (RSSI alone) is feasible-but-bounded. RSSI is a band-aggregate scalar that loses per-subcarrier resolution. If saliency had been concentrated in 1–2 narrow regions, RSSI's information ceiling would be very low. Because the signal is band-spread, RSSI retains the integral and the ceiling is meaningfully higher than feared — first-order estimate: ~60% of full-CSI accuracy upper-bound based on this saliency distribution.
3. R7 (adversarial defence) priority list. The top-8 saliency subcarriers are exactly the ones a defender must corroborate across nodes — an attacker who spoofs uniformly will be most-easily-caught here.

Next steps in this thread (queued for later ticks)

• Retrain at K=8, K=16, K=32 → publish accuracy-vs-K curve.
• Same saliency map for the pose model.
• Compare K=8 subset across two independent recordings → does the same K=8 set rank highest?
• Cross-reference with wifi-densepose-signal's existing subcarrier selection in subcarrier.rs.