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mariamismael/
signal-atlas-final

Name: signal-atlas-final
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Signal Atlas — LTE Signal Coverage Predictor

Signal Atlas is a crowdsourced LTE signal quality mapping system developed at Zewail City of Science and Technology. This repository contains the final trained model for predicting RSRP and RSRQ at unmeasured campus locations.

Model Overview

Property	Value
Model type	K-Nearest Neighbors (KNN)
K	7
Task	Spatial regression (RSRP + RSRQ prediction)
Training points	725 unique campus locations
Data cutoff	After 2026-04-25 (cell ID enabled app)

Features

Feature	Description
`latitude`	GPS latitude of measurement
`longitude`	GPS longitude of measurement
`pci_encoded`	Physical Cell ID (encoded) — identifies serving cell sector
`dist_to_proxy`	Distance to strongest measured point per PCI (meters)
`cell_id_encoded`	Unique cell sector identifier (encoded)

Evaluation Results

Evaluated using gap-based spatial split — tests boundary points at cluster edges to simulate real interpolation conditions.

Metric	Value
RSRP MAE	4.41 dBm
RSRP RMSE	5.61 dBm
RSRP R²	0.5404
Within ±10 dBm	92.5%
Train / Test split	619 / 106 points

How to Use

import joblib

bundle   = joblib.load("signal_atlas_knn.joblib")
knn_rsrp = bundle["knn_rsrp"]
knn_rsrq = bundle["knn_rsrq"]
scaler   = bundle["scaler"]
le_pci   = bundle["le_pci"]
le_cell  = bundle["le_cell"]

def predict(lat, lon, pci, cell_id, dist_to_proxy):
    pci_enc  = le_pci.transform([str(pci)])[0] if str(pci) in le_pci.classes_ else 0
    cell_enc = le_cell.transform([str(cell_id)])[0] if str(cell_id) in le_cell.classes_ else 0
    X = scaler.transform([[lat, lon, pci_enc, dist_to_proxy, cell_enc]])
    return knn_rsrp.predict(X)[0], knn_rsrq.predict(X)[0]

rsrp, rsrq = predict(29.9413, 31.0668, 289, 54450177, 150.0)
print("RSRP:", round(rsrp, 1), "dBm | RSRQ:", round(rsrq, 1), "dB")

Repository Structure

signal-atlas-final/
├── signal_atlas_knn.joblib   <- Full model bundle (models + encoders + scaler)
├── model_info.json           <- Training metadata and evaluation metrics
└── README.md                 <- This file

Project Context

Signal Atlas collects LTE signal measurements from Android devices across Zewail City campus. The ML module fills coverage gaps by predicting signal quality at unmeasured locations using spatial interpolation.

Key findings:

84.5% of raw readings are exact coordinate duplicates — deduplication is essential
Cell ID is the most important feature (GBM importance: 0.349)
Gap-based evaluation is the most honest method for interpolation tasks
MAE of 4.41 dBm is competitive with published crowdsourced RSRP models

Team: Hamza Mohammed, Salma Hatem, Mariam Ismael, Mohamed Mahmoud
Supervisor: Dr. Mahmoud Abdelaziz
Program: Communications and Information Engineering, Zewail City
Year: 2025/2026

mariamismael/signal-atlas-final