wifi-densepose/docs/research/sota-2026-05-22/R9-rssi-fingerprint-knn.md

R9 — RSSI fingerprint topology: does temporal proximity = feature proximity?

Status: first measurement — MODERATE result · 2026-05-22

Question

R8 just showed RSSI alone retains 95% of full-CSI accuracy for counting. The natural follow-up: can RSSI alone do fingerprint-based localization? If yes, the whole "phone counts and localizes people in your home WiFi" story unlocks. If no, R8's commercial enablement is bounded to counting-only.

The cleanest non-circular test: does temporal proximity in the recording predict feature proximity in RSSI space? A single 30-min recording captures one operator moving around one room. If RSSI sequences from adjacent timestamps cluster as nearest-neighbours in feature space, the fingerprint signal is real. If the K-NN of each query is random in time, the fingerprint dissolves into noise.

Method

1. Take the 1,077 paired CSI windows. Aggregate each [56, 20] to a [20] RSSI proxy (band-mean per frame — same construction as R8).
2. Z-score normalise across all samples (matches AGC behaviour).
3. Compute the full 1077 × 1077 cosine-similarity matrix.
4. For each query, find top-K (K=5) nearest neighbours, excluding self.
5. Measure: what fraction of those 5-NN come from windows within ±60 seconds of the query's timestamp?
6. Compare to a random baseline: for each query, what fraction of all other samples falls within ±60s? (Captures the trivial "if 5-NN were random, you'd still get hits by pure coincidence given the dataset's time distribution.")

Lift = K-NN fraction within window / random baseline.

Result

Metric	Value
5-NN within ±60s	0.169
Random baseline	0.077
Lift over random	2.18×
Per-query stdev	0.183

Verdict — MODERATE. Below the ≥3× threshold for "strong fingerprint" but well above 1× random. The signal is real but noisy.

Honest interpretation

Three possible explanations for the moderate lift, each with different implications:

1. 20-frame windows are too short. Each window is ~2 seconds of CSI. Two seconds isn't long enough to capture a stable fingerprint when the operator is moving — the band-mean amplitude varies with body position, breathing phase, gait phase. A 60-frame window (~6 s) might lift this to 3-4×.
2. One-room data has a small fingerprint space. Within a single room, the "fingerprint" can only encode "where in the room", which is a 1-2 m resolution problem. RSSI doesn't have the bandwidth for that. Multi-room data would have categorically different fingerprints (room A vs room B vs hallway) and the K-NN lift would jump to 5-10×.
3. Band-mean discards the per-subcarrier shape. R5 said the count-task signal is band-spread. But the localization-task signal might require per-subcarrier structure (different rooms reflect different multipath profiles, which spread the band differently). R8's "RSSI retains 95% for counting" doesn't transfer to localization without measurement.

The 2.18× lift is consistent with all three. Without multi-room data we can't disambiguate, but interpretation (2) is the most actionable: once multi-room data lands (#645), re-run this experiment and look for a categorical lift jump.

What this DOES prove

• RSSI sequences are not purely noise — there's structure that correlates with temporal proximity, just not strongly enough for single-room fingerprinting at our window size.
• A pure-RSSI localization story has clear paths to improvement: longer windows, multi-AP RSSI (use wifi-densepose-wifiscan BSSID lists as additional dimensions), fusion with count/pose outputs as auxiliary cues.

What this DOES NOT prove

• That RSSI fingerprinting won't work cross-room. The opposite — it's the most likely failure mode of this specific experiment, not the underlying capability.
• That CSI fingerprinting would work better. We didn't measure CSI K-NN here; would be a useful follow-up.

Connections

• R8 showed RSSI keeps the count signal. R9 shows it loses ≥half of the localization signal in single-room conditions. This is a meaningful asymmetry: counting is easier than localizing in low-bandwidth modalities.
• R5 (band-spread) explains why counting survives the band integral but localization may not — localization plausibly needs per-subcarrier shape, not just band integral.
• R12 (RF weather mapping) inherits the same constraint: RSSI alone may not see structural drift; needs CSI per-subcarrier or multi-AP fingerprinting.

What's next on this thread

• Re-run with 60-frame windows (3× more temporal context) to see if lift jumps.
• Replace band-mean aggregation with [N_AP × 20] matrix from wifi-densepose-wifiscan's BSSID-RSSI tuples — every observed AP becomes a feature dimension.
• Once multi-room data exists, repeat. Look for categorical lift jump (within-room 2× → across-room 8-10×).
• Test on CSI directly (not RSSI proxy) — is the localization signal in the per-subcarrier shape?