You can deploy this model and start earning money today!

uts-aiot-ibc-research/
graph-xgb

Views

No views yet

graph-xgb — Indoor 5G Signal Prediction (Spider-GNN + XGBoost)

A two-stage hybrid model that predicts five indoor 5G RF parameters (RSSI, RSRP, RSRQ, SNR, CQI) from sparse outdoor + indoor walk-test measurements at a single multi-floor building.

The GNN backbone (Spider-GNN, BESC2025) learns a 128-D graph embedding over a spatial KNN graph of measurement points. PCA reduces it to 10 D, which is concatenated with 14 raw spatial features and fed to a per-target ensemble of 9 XGBoost regressors (one seed per estimator, predictions averaged at inference).

Files

File	Purpose
`gnn.pt`	Spider-GNN backbone state_dict (141 K params, ~579 KB)
`pca.pkl`	Pickled `{"pca": sklearn.PCA(n=10), "scaler": StandardScaler}`
`xgb.pkl`	Pickled `dict[target_name, list[XGBRegressor]]` — 9 seeds × 5 targets

Tag the model with revision="v8" when downloading (see Usage below).

Test-set performance (v8)

Evaluated on a held-out 58-node subset (single building, 613 nodes, 9 668 edges; train 268 / val 58 / test 58).

Target	R² ↑	RMSE	Unit
RSSI	0.759	6.43	dBm
RSRP	0.770	6.36	dBm
RSRQ	0.268	1.70	dB
SNR	0.146	6.06	dB
CQI	0.195	2.86	step
Mean R²	0.421	—	—

v8 is currently the best-on-mean configuration across v2–v9; v9 (cascaded ensemble) is competitive on CQI but loses on SNR.

Usage

python

import pickle
import torch
from huggingface_hub import hf_hub_download

REPO = "uts-aiot-ibc-research/graph-xgb"
REV  = "v8"

gnn_pt  = hf_hub_download(REPO, "gnn.pt",  revision=REV)
pca_pkl = hf_hub_download(REPO, "pca.pkl", revision=REV)
xgb_pkl = hf_hub_download(REPO, "xgb.pkl", revision=REV)

# 1. Load GNN backbone (architecture: GATv2Conv ×2, hidden=32, heads=4, in_dim=14)
#    See `apps/api/src/graph_xgb/registry/hf_loader.py` in the source repo
#    (uts-aiot-ibc-research/spider-gnn) for the full `_SpiderGNN` definition.
state_dict = torch.load(gnn_pt, map_location="cpu", weights_only=True)

# 2. Load PCA + StandardScaler
with open(pca_pkl, "rb") as f:
    bundle = pickle.load(f)
pca, scaler = bundle["pca"], bundle["scaler"]

# 3. Load 45 XGBoost regressors (5 targets × 9 seeds)
with open(xgb_pkl, "rb") as f:
    ensemble = pickle.load(f)   # dict[target, list[XGBRegressor]]

# Inference pipeline:
#   emb  = gnn.get_embedding(x, edge_index, edge_attr)   # (N, 128)
#   pca_emb = pca.transform(scaler.transform(emb))       # (N, 10)
#   X    = np.concatenate([pca_emb, x_spatial], axis=1)  # (N, 24)
#   for target in ["CQI", "RSSI", "RSRP", "SNR", "RSRQ"]:
#       preds = np.mean([m.predict(X) for m in ensemble[target]], axis=0)

For a turn-key FastAPI server + Next.js viz, see the source repository.

Architecture

Spider-GNN backbone (frozen after training)
└─ GATv2Conv(14 → 32×4=128, edge_dim=4)
   GATv2Conv(128 → 128, edge_dim=4)
   LayerNorm + GELU + Dropout(0.3)
   shared_mlp → 128-D embedding
   per-target heads (RSSI / RSRP / RSRQ / SNR / CQI)
     ↑ heads are trained but unused at inference;
       only the shared 128-D embedding feeds the XGBoost stage.

XGBoost stage (per target, ensemble of 9 seeds)
└─ Input: 24 D = PCA(128 → 10) + 14 D raw spatial features
   Output: averaged prediction in physical units
           (CQI clipped and rounded to [0, 15])

Training data

Dataset: 613 nodes (384 internal + 229 external context) from a single multi-floor indoor building campaign. Walk-test measurements combined from outdoor (calibrated) and indoor (relative) RF surveys. Not publicly redistributed.

Limitations

Single-building generalisation — model is fit to one indoor venue's geometry and KNN graph structure; transferring to another building requires retraining on a new graph.
Interference-bounded targets — SNR (0.146) and CQI (0.195) hit a ceiling because the input features do not include interference measurements; this is a data limitation, not an architectural one.
Frozen-graph inference only — adding new survey points requires reconstructing the KNN graph (edge_index, edge_attr) with the original scaler statistics.

Citation

bibtex

@inproceedings{spidergnn2025,
  title     = {Spider-GNN: A Graph Neural Network for Indoor 5G Signal Prediction},
  booktitle = {BESC 2025},
  year      = {2025},
  note      = {Code and model: https://huggingface.co/uts-aiot-ibc-research/graph-xgb}
}

License

MIT — see source repository.