Analytics Dashboard — Cellular Statistic Management System

Date Range

Year

Month

Start Date

End Date

City

Owner Market Share

Indonesia (Global)

Owner	Sites	Share

Top Operators

By cell count

Operator	Region	Cells	RSRP	RSRQ

UE Measurements

—

Logical Cells

—

Physical Sites

—

Avg RSRP

—

Measurement Trend

Last 7 days

Signal Quality Trend

Last 7 days

Signal Quality Distribution

RSRP Quality Ranges

Total Measurements

—

Avg RSRP

—

Avg RSRQ

—

Alerts

Theoretical Coverage (POI)

Model-based RSRP estimation (terrain-aware) for critical locations

City

Total POI

—

Excellent (≥-80)

—

Good (-81 to -100)

—

Fair+Poor (<-100)

—

City	Total POI	Avg RSRP	Coverage Distribution	Score

District	Total POI	Avg RSRP	Coverage Distribution	Score

Best Coverage (Top 15)

Subdistrict	POI	Avg RSRP

Needs Improvement (Top 15)

Subdistrict	POI	Avg RSRP

POI Type	Count	Avg RSRP	Min	Max	Distribution

Critical POIs requiring attention - These locations have the weakest signal coverage and should be prioritized for network improvements.

POI Name	Type	Location	RSRP	Status	Serving Tower

Real-time UE RSRP Aggregation

Crowd-sourced measurements from Drive Test

City

Total Measurements

—

Excellent (≥-80)

—

Good (-81 to -100)

—

Fair+Poor (<-100)

—

City	Measurements	Avg RSRP	Coverage Distribution	Score

District	Measurements	Avg RSRP	Coverage Distribution	Score

Best Coverage (Top 15)

Subdistrict	Measurements	Avg RSRP

Needs Improvement (Top 15)

Subdistrict	Measurements	Avg RSRP

Coverage Comparison

Theoretical Coverage (POI) vs Drive Test (Real UE)

City

City	Theoretical (POI)		Real-time UE		Difference (Real - Model)
City	Quantity	Average RSRP	Measurements	Average RSRP	Difference (Real - Model)

District	Theoretical (POI)		Real-time UE		Difference (Real - Model)
District	Quantity	Average RSRP	Measurements	Average RSRP	Difference (Real - Model)

Showing top 20 subdistricts with largest differences between theoretical and real-time measurements

Subdistrict	District	Theoretical (POI)		Real-time UE		Difference (Real - Model)
Subdistrict	District	Quantity	Average RSRP	Measurements	Average RSRP	Difference (Real - Model)

Poor Signal & Blankspot

Areas with weak or no coverage based on UE RSRP measurements

City

City	Total Measurements	Avg RSRP	Poor Signal (< -100 dBm)	Very Poor (< -115 dBm)	Distribusi

District	Total Measurements	Avg RSRP	Poor Signal (< -100 dBm)	Very Poor (< -115 dBm)	Distribution

Showing areas with poorest signal quality (ordered by worst average RSRP)

Subdistrict	District	Total Measurements	Avg RSRP	Blankspot (-150 dBm)	Poor Signal (< -115 dBm)	Status

Theoretical Foundation & Analysis Methodology

Despite rigorous modeling standards, predictive coverage values are subject to significant deviations from actual in-field conditions. This variance stems from complex multifactorial radio-propagation phenomena:

Scientific Basis

Cost-Hata Propagation Model: Industry-standard empirical path loss model validated across cellular frequency bands (800–2600 MHz), incorporating terrain morphology and clutter categorization per ITU recommendations.
ITU-R Recommendations: Propagation models (P.1546-5, P.1812-4) providing deterministic and statistical coverage prediction methodologies aligned with 3GPP standardized site-planning procedures.
3GPP Technical Specifications: Coverage and capacity planning tools (3GPP TS 36.942 for LTE, 3GPP TS 38.901 for 5NR) defining link budget calculations, cell-edge thresholds, and inter-cell interference coordination.
Measurement-Based Validation: Drive-test data correlation (RSRP, RSRQ, SINR measurements) against theoretical predictions to calibrate models and identify planning-reality misalignments.

Analytical Limitations & Inherent Uncertainties

Primary Deviation Sources

Terrain & Morphology: High-resolution digital elevation models may not capture micro-topography, urban canyon effects, or transient obstacles (construction, vegetation growth). Clutter databases require continuous updates.
Environmental Factors: Weather-dependent attenuation (rain-induced absorption at higher frequencies), seasonal foliage density variations, and dynamic urban landscape changes introduce non-deterministic path losses.
User Equipment (UE) Variability: Device-specific antenna patterns, tuning efficiency, modem implementation variations, and user handling practices (grip/shielding) cause 3–8 dB measurement variance independent of network performance.
Propagation Anomalies: Atmospheric ducting, diffraction complexities in dense urban areas, multipath fading patterns, and interference from adjacent operators create localized prediction errors ±15 dB in worst-case scenarios.
Temporal Dynamics: Network congestion, cell load fluctuations, handover overhead, and inter-cell interference variations produce time-dependent performance degradation not captured in quasi-static models.
Data Quality & Sampling Bias: Drive-test route selection may not uniformly sample geographic regions; sparse measurements in rural areas introduce statistical uncertainty; measurement timing coincides with specific traffic profiles.

Recommended Application Framework

This system's outputs are analytical decision-support tools, not absolute performance predictors. Responsible deployment practices mandate:

Validation Through Measurement: Systematic drive-test campaigns in target deployment areas (minimum 3–5 km per sub-district) to establish baseline coverage signatures and validate theoretical predictions within ±5 dB confidence intervals.
Multi-Stakeholder Collaboration: Cross-functional engagement involving RF Planning engineers, Field Operations teams, Regulatory Affairs specialists, and Real Estate divisions to harmonize network planning with infrastructure constraints and regulatory mandates.
Iterative Refinement: Deploy network interventions (new sites, parameter optimization, spectrum refarming) and conduct post-implementation measurement audits to quantify actual improvement and recalibrate predictive models.
Scenario Analysis: Utilize Monte Carlo simulations and sensitivity analyses to quantify confidence intervals around central predictions, establishing acceptable risk thresholds for business-critical deployments.
Regulatory Compliance: Ensure all planning outputs satisfy national frequency allocation policies, environmental impact assessments, and operator licensing obligations before implementation approval.
Documentation & Traceability: Maintain audit trails recording model versions, input datasets, propagation parameters, and assumption changes to enable transparent reproducibility and accountability.

Disclaimer: This system is provided for strategic planning support only. Operators remain solely responsible for independent validation, regulatory compliance, and performance accountability. The developers disclaim liability for inaccuracies, omissions, or consequential losses arising from reliance on system outputs without independent verification.