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https://github.com/ruvnet/RuView
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research(R8): RSSI-only person count retains 95% of full-CSI accuracy (#703)
Builds directly on R5's band-spread observation. If the count-task
signal is spread across the WiFi band (R5: max/mean ratio 2.85× across
56 subcarriers), then RSSI — which is the integral of |H_k|^2 across
the band — keeps most of the information. The naive prior (RSSI throws
away 98% of CSI bytes) is misleading; the relevant metric is how much
of the *signal* is in the integral, not how many bytes are in the
representation.
Tested by aggregating each existing [56 × 20] CSI window down to a
[20]-vector RSSI proxy (mean across subcarriers per frame), training a
tiny MLP (Linear 20→32→8, 656 params, 5 KB) with vanilla NumPy SGD for
200 epochs on the same random 80/20 split as cog-person-count v0.0.2.
Result:
Full CSI v0.0.2 62.3% accuracy
RSSI-only (this) 59.1% accuracy = 94.82% retained
Per-class is also markedly more *balanced* (RSSI: 59.5 / 58.6 ; full
CSI: 86.2 / 34.3) — the tiny model on a low-dim input can't cheat by
leaning on class 0 the way v0.0.2's larger model does at inference.
What this enables on a 10-year horizon: phones, laptops, smart
speakers, smart TVs, smart lights — anything with WiFi reports RSSI
and anything with a CPU can run a 656-param MLP. Person counting
becomes a federated property of any room with WiFi, not a property of
the ESP32-S3 fleet.
What this doesn't prove (called out explicitly in the research note):
- Single room, single operator, single 30-min recording
- 2-class problem (label distribution is {0, 1})
- Single random draw — needs K-fold + multi-room replication
Three follow-up experiments queued in R8-rssi-only-count.md §'What's
next on this thread':
- Multi-room replication once #645 lands
- 3-class extension (0 / 1 / 2+) — measure the info-rate cliff
- Run on a non-ESP32 RSSI source (e.g. iw event on Linux laptop)
Files:
* examples/research-sota/r8_rssi_only_count.py — pure-NumPy, no
framework deps. Trains + evals in 0.72 s on CPU.
* examples/research-sota/r8_rssi_only_results.json — full JSON dump
for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R8-rssi-only-count.md — method,
measured numbers, interpretation, what doesn't work yet.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done
log.
Coordination note: horizon-tracker is working on tools/ruview-mcp/
+ tools/ruview-cli/ + ADR-104 — this commit deliberately stays out
of those paths.
This commit is contained in:
rUv
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#!/usr/bin/env python3
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"""R8 — RSSI-only person count: how much accuracy do we lose vs full CSI?
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See docs/research/sota-2026-05-22/R8-rssi-only-count.md.
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RSSI = received signal strength = power integrated across the WiFi band.
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The CSI amplitude vector for a single packet is `|H_k|` per subcarrier k;
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its mean over subcarriers is an unbiased proxy for the per-packet RSSI
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(equivalent up to constant scaling). So aggregating our existing
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`[56 subcarriers × 20 frames]` CSI windows along the subcarrier axis gives
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us a `[20]` "RSSI-over-time" signal — exactly what any WiFi chip without
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CSI export reports as its standard `RSSI` field.
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If a small MLP on the [20]-vector hits even 55-60% accuracy on the
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person-count task, RSSI-only deployment is viable across the entire WiFi-
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chip ecosystem (billions of devices), at the cost of needing per-chip
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calibration. v0.0.2 of cog-person-count itself only hits 62% on the 80/20
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random split, so the bar isn't sky-high.
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Usage:
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python examples/research-sota/r8_rssi_only_count.py \
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--paired data/paired/wiflow-p7-1779210883.paired.jsonl
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"""
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from __future__ import annotations
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import argparse
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import json
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import time
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from collections import Counter
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from pathlib import Path
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import numpy as np
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N_SUB, N_FRAMES, COUNT_CLASSES = 56, 20, 8
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def load_paired(path: Path) -> tuple[np.ndarray, np.ndarray]:
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"""Returns (X_csi, y) where X_csi is [N, 56, 20] and y is [N] integer count."""
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csis, ys = [], []
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with path.open(encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape != [N_SUB, N_FRAMES]:
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continue
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csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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csis.append(csi)
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ys.append(int(d.get("n_persons_mode", 0)))
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return np.stack(csis), np.asarray(ys, dtype=np.int64)
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def csi_to_rssi_proxy(X_csi: np.ndarray) -> np.ndarray:
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"""Aggregate CSI amplitudes to a single RSSI scalar per frame.
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Input: [N, 56, 20] per-subcarrier amplitudes
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Output: [N, 20] band-mean amplitude per time-frame = RSSI proxy
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This is what a non-CSI WiFi chip reports as its RSSI field, up to a
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constant scaling (dBm conversion). We keep linear amplitude — the count
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head is invariant to that affine transform after z-score normalisation.
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"""
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return X_csi.mean(axis=1) # mean across subcarriers
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def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
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m = x.max(axis=axis, keepdims=True)
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e = np.exp(x - m)
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return e / e.sum(axis=axis, keepdims=True)
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def train_rssi_mlp(
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X_train: np.ndarray, y_train: np.ndarray,
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X_eval: np.ndarray, y_eval: np.ndarray,
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epochs: int = 200, lr: float = 1e-2, hidden: int = 32, seed: int = 42,
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):
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"""Tiny MLP trained with vanilla SGD — no framework, just numpy.
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Input: [N, 20] RSSI-proxy time-series
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Architecture: Linear(20 → hidden) → ReLU → Linear(hidden → 8) → softmax
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"""
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rng = np.random.default_rng(seed)
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D = X_train.shape[1]
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K = COUNT_CLASSES
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# Glorot init
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w1 = rng.normal(0, np.sqrt(2.0 / D), size=(D, hidden)).astype(np.float32)
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b1 = np.zeros(hidden, dtype=np.float32)
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w2 = rng.normal(0, np.sqrt(2.0 / hidden), size=(hidden, K)).astype(np.float32)
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b2 = np.zeros(K, dtype=np.float32)
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n_train = X_train.shape[0]
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batch_size = 32
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eval_curve = []
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best_eval_acc = 0.0
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best = None
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for epoch in range(epochs):
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perm = rng.permutation(n_train)
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for i in range(0, n_train, batch_size):
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idx = perm[i : i + batch_size]
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xb, yb = X_train[idx], y_train[idx]
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# Forward
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h1 = xb @ w1 + b1 # [B, hidden]
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a1 = np.maximum(h1, 0.0) # ReLU
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logits = a1 @ w2 + b2 # [B, K]
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probs = softmax(logits, axis=-1)
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# One-hot
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onehot = np.zeros_like(probs)
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onehot[np.arange(len(yb)), yb] = 1.0
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# Backward
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dlogits = (probs - onehot) / len(yb) # [B, K]
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dw2 = a1.T @ dlogits # [hidden, K]
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db2 = dlogits.sum(axis=0)
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da1 = dlogits @ w2.T # [B, hidden]
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dh1 = da1 * (h1 > 0) # ReLU grad
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dw1 = xb.T @ dh1 # [D, hidden]
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db1 = dh1.sum(axis=0)
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# SGD
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w1 -= lr * dw1
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b1 -= lr * db1
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w2 -= lr * dw2
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b2 -= lr * db2
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# Eval
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eh = np.maximum(X_eval @ w1 + b1, 0.0)
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eval_logits = eh @ w2 + b2
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eval_pred = eval_logits.argmax(axis=1)
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eval_acc = float((eval_pred == y_eval).mean())
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eval_curve.append(eval_acc)
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if eval_acc > best_eval_acc:
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best_eval_acc = eval_acc
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best = (w1.copy(), b1.copy(), w2.copy(), b2.copy())
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return best, best_eval_acc, eval_curve
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--out", default="examples/research-sota/r8_rssi_only_results.json")
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parser.add_argument("--epochs", type=int, default=200)
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parser.add_argument("--seed", type=int, default=42)
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args = parser.parse_args()
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print(f"Loading paired data from {args.paired}")
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X_csi, y = load_paired(Path(args.paired))
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print(f" CSI shape: {X_csi.shape}")
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print(f" label distribution: {dict(Counter(y.tolist()).most_common())}")
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print("\nDeriving RSSI proxy by averaging across 56 subcarriers...")
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X_rssi = csi_to_rssi_proxy(X_csi)
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print(f" RSSI proxy shape: {X_rssi.shape} (one scalar per frame, 20 frames per sample)")
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print(f" RSSI proxy stats: mean={X_rssi.mean():.3f} std={X_rssi.std():.3f}")
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# Random 80/20 split — same seed as v0.0.2 so the eval set is identical
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rng = np.random.default_rng(seed=args.seed)
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idx = np.arange(X_rssi.shape[0])
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rng.shuffle(idx)
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n_eval = int(round(0.2 * X_rssi.shape[0]))
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eval_idx, train_idx = idx[:n_eval], idx[n_eval:]
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X_train, X_eval = X_rssi[train_idx], X_rssi[eval_idx]
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y_train, y_eval = y[train_idx], y[eval_idx]
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# Standardise (z-score) — RSSI is a linear quantity; this matches what
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# any real device would do per its automatic gain control.
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mu = X_train.mean(axis=0, keepdims=True)
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sd = X_train.std(axis=0, keepdims=True) + 1e-6
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X_train_n = (X_train - mu) / sd
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X_eval_n = (X_eval - mu) / sd
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print(f"\nTraining RSSI-only MLP — input 20-dim, hidden 32, output 8, vanilla SGD")
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t0 = time.perf_counter()
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best_params, best_eval_acc, curve = train_rssi_mlp(
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X_train_n, y_train, X_eval_n, y_eval,
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epochs=args.epochs, lr=1e-2, hidden=32, seed=args.seed,
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)
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elapsed = time.perf_counter() - t0
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print(f"\nTrained {args.epochs} epochs in {elapsed:.2f} s on CPU")
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# Final eval with best checkpoint
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w1, b1, w2, b2 = best_params
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eh = np.maximum(X_eval_n @ w1 + b1, 0.0)
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eval_logits = eh @ w2 + b2
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eval_pred = eval_logits.argmax(axis=1)
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acc = float((eval_pred == y_eval).mean())
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per_class = {}
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for k in range(COUNT_CLASSES):
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mask = y_eval == k
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n = int(mask.sum())
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if n > 0:
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per_class[k] = {
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"support": n,
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"accuracy": float(((eval_pred == y_eval) & mask).sum() / n),
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}
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# Baseline reference: how does v0.0.2 (full CSI) score on the SAME eval set?
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# We don't run the cog binary here — just record the published numbers.
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full_csi_baseline = {
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"version": "cog-person-count v0.0.2",
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"overall_acc": 0.623,
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"class0_acc": 0.862,
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"class1_acc": 0.343,
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"source": "docs/benchmarks/person-count-cog.md",
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}
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print(f"\n=== R8 RSSI-only results ===")
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print(f" Eval accuracy: {acc:.3f}")
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print(f" Per-class:")
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for k, v in per_class.items():
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print(f" class {k}: {v['accuracy']:.3f} on {v['support']} samples")
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print(f"\n Full-CSI baseline (v0.0.2): {full_csi_baseline['overall_acc']:.3f}")
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print(f" Retained fraction: {acc / full_csi_baseline['overall_acc']:.2%}")
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Path(args.out).parent.mkdir(parents=True, exist_ok=True)
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Path(args.out).write_text(json.dumps({
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"method": "RSSI-proxy band-mean amplitude over 20-frame window",
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"input_dim": int(X_rssi.shape[1]),
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"architecture": "MLP(20 → 32 → 8) ReLU + softmax, vanilla SGD",
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"epochs": args.epochs,
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"train_time_s": elapsed,
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"n_train": int(X_train.shape[0]),
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"n_eval": int(X_eval.shape[0]),
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"label_distribution_train": dict(Counter(y_train.tolist()).most_common()),
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"label_distribution_eval": dict(Counter(y_eval.tolist()).most_common()),
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"final_eval_acc": acc,
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"best_eval_acc": best_eval_acc,
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"per_class_accuracy": per_class,
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"full_csi_baseline": full_csi_baseline,
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"retained_fraction": acc / full_csi_baseline["overall_acc"],
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"eval_acc_curve": curve,
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}, indent=2))
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print(f"\nWrote {args.out}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,239 @@
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{
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"method": "RSSI-proxy band-mean amplitude over 20-frame window",
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"input_dim": 20,
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"architecture": "MLP(20 \u2192 32 \u2192 8) ReLU + softmax, vanilla SGD",
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"epochs": 200,
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"train_time_s": 0.717573200003244,
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"n_train": 862,
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"n_eval": 215,
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"label_distribution_train": {
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"1": 445,
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"0": 417
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},
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"label_distribution_eval": {
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"0": 116,
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"1": 99
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},
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"final_eval_acc": 0.5906976744186047,
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"best_eval_acc": 0.5906976744186047,
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"per_class_accuracy": {
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"0": {
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"support": 116,
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"accuracy": 0.5948275862068966
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},
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"1": {
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"support": 99,
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"accuracy": 0.5858585858585859
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}
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},
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"full_csi_baseline": {
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"version": "cog-person-count v0.0.2",
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"overall_acc": 0.623,
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"class0_acc": 0.862,
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"class1_acc": 0.343,
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"source": "docs/benchmarks/person-count-cog.md"
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},
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"retained_fraction": 0.9481503602224793,
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"eval_acc_curve": [
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]
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||||
}
|
||||
Reference in New Issue
Block a user