Detecting Network Inconsistencies with Atoll Live: A Technical Guide

Radio network planning is a complex, data-intensive process. Even minor inconsistencies such as antenna misalignments, overshooting cells, or location discrepancies can degrade network performance, increase interference, and impact customer experience. Atoll Live is designed to automate the detection and resolution of these issues, ensuring that your network operates at peak efficiency.

In this article, we’ll explore how Atoll Live detects network inconsistencies, the technical workflows behind its analytics engine, and how Naos automates the resolution of these issues.

Why Network Inconsistencies Matter

Network inconsistencies are silent killers of performance. They often go unnoticed until they cause coverage gaps, interference, or customer complaints. Common inconsistencies include:

• Antenna location discrepancies: Incorrect coordinates in the database.
• Antenna orientation misalignments: Azimuth or tilt values that don’t match real-world conditions.
• Overshooting cells: Cells that propagate too far, causing interference.
• Coverage gaps: Areas where predicted coverage doesn’t match real-world performance.

These issues lead to:

• Poor customer experience: Dropped calls, slow data speeds, and inconsistent coverage.
• Increased costs: Manual effort to detect and resolve issues, over-provisioning of sites.
• Regulatory non-compliance: Failure to meet minimum coverage thresholds.

How Atoll Live Detects Network Inconsistencies

Atoll Live’s live analytics engine automatically detects network inconsistencies by comparing predicted coverage with real-world measurements. Here’s how it works:

1. Data Integration

Atoll Live integrates multiple data sources to create a real-time view of network performance:

• Crowdsourced data: User measurements from mobile devices.
• MDT (Minimisation of Drive Tests): Geolocated measurements from network probes.
• Drive tests: Trusted, high-accuracy measurements from field tests.
• OSS parameters: Power levels, sector configurations, and antenna settings from OSS systems (Ericsson, Nokia, Huawei).

2. Live Analytics Engine

Atoll Live’s analytics engine processes this data to detect inconsistencies. Key features include:

• Antenna Location Discrepancy Detection: 
  • Compares database coordinates with real-world measurements to identify misaligned sites.
  • Uses Timing Advance (TA) values to estimate the most likely antenna position.
• Antenna Orientation Discrepancy Detection:
  • Compares predicted azimuth with real-world user measurements to detect misalignments.
  • Calculates the dominant direction of user traffic for better service quality.
• Overshooting Cell Detection:
  • Uses statistical approach to flag cells risking to degrade service quality and hinder mobility management.
  • Analyses Timing Advance histograms to identify non-contiguous coverage.

3. Visualization and Reporting

Atoll Live provides dedicated coverage maps and KPI dashboards to visualize inconsistencies:

• Coverage maps: Highlight areas where predicted coverage doesn’t match real-world measurements.
• KPI dashboards: Show metrics like location discrepancy, orientation discrepancy, and overshooting cells. 
• Table views: List affected cells and recommended corrective actions.

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Technical Workflows for Issue Detection

Let’s dive into the technical workflows for detecting and resolving network inconsistencies with Atoll Live.

1. Antenna Location Discrepancy Detection

Workflow:

1. Import live measurements (crowdsourced data, MDT, drive tests) into Atoll.
2. Run live analytics to compare database coordinates with real-world measurements.
3. Flag discrepancies where the distance between predicted and actual antenna positions exceeds a threshold (e.g., 50 meters).
4. Estimate the most likely antenna position using Timing Advance values.  

Example:

• Issue: A cell’s predicted coverage doesn’t match user measurements, indicating a location discrepancy.  
• Solution:  Atoll Live Analytics detects an antenna location issue and suggest the most likely antenna position

2. Antenna Orientation Discrepancy Detection

Workflow:

1. Import live measurements into Atoll.
2. Run live analytics to compare predicted azimuth with real-world user measurements.
3. Flag discrepancies where the difference exceeds a threshold (e.g., 65 degrees).
4. Calculate the dominant direction of user traffic to identify backlobe coverage.
5. Suggests azimuth to align with user demand.

Example:

• Issue: A cell’s predicted coverage shows backlobe interference, indicating a misaligned antenna. 
• Solution: Atoll Live flags the issue and recommends an azimuth adjustment to reduce interference.

3. Overshooting Cell Detection

Workflow:

1. Import live measurements into Atoll.
2. Run live analytics to detect overshooting cells. 
3. Flag cells with non-contiguous coverage using outlier detection method. 
4. Adjust tilt, power, or azimuth manually or using the ACP to reduce overshooting.
5. Refine neighbor list to improve handover performance.

Example:

• Issue: A cell’s overshooting indicator shows non-contiguous coverage, indicating the cause of the service degradation.
• Solution: Atoll Live flags the cell and which can be optimized using tilt or power adjustments to contain coverage.

Automating Issue Resolution with Naos

Naos Live automates network performance improvements based on user experience data. Naos workflows can:

• Combine predicted path loss with live measurements to refine coverage predictions.
• Automate parameter adjustments (e.g., tilt, power, azimuth) based on live data.
• Generate combined coverage maps for planning and optimisation.

Example: Automated Customer-centric Path Loss 

1. Load network data (sites, cells, equipment) into Naos.
2. Integrate live measurements (crowdsourced data, MDT) to refine path loss matrices.
3. Run Naos workflows to generate combined coverage maps.  
4. Export results for planning, optimisation, and regulatory reporting.

Result: 90% reduction in manual effort and 5x faster coverage accuracy improvement. 

Key Features of Atoll Live and Naos Live

Business Implications of Live Data Integration

While Atoll Live and Naos Live are technical tools, their impact extends to business outcomes:

• Cost Savings:
  • Reduce manual effort for issue detection and resolution.
  • Avoid over-provisioning of sites and equipment.
• Regulatory Compliance:
  • Ensure coverage meets regulatory thresholds with real-world measurements.
  • Provide auditable reports for regulatory submissions.
• Customer Satisfaction:
  • Improve network performance and reliability.
  • Align network design with real user demand.

Customer Spotlight: Tier 1 Operator in North America

A Tier 1 operator in North America deployed Atoll Live and Naos Live to optimize its 5G network. The results:

• 90% reduction in manual effort for issue detection.
• 8x faster coverage accuracy improvement.
• $2M annual savings in operational costs.

The Future: AI and Closed-Loop Automation

The future of radio network planning lies in Customer-centric AI-driven automation. Here’s what’s next:

• AI for Predictive Analytics: Use AI to predict network performance and proactively resolve issues.
• Closed-Loop Automation: Integrate Naos with Open RAN architectures (SMO, RIC) for self-healing networks.
• Customer-Centric OptimisationOptimisation: Tailor network performance to specific user segments (e.g., urban vs. rural).

Conclusion: Automate Your Network Planning Today

Network inconsistencies are inevitable, but they don’t have to degrade performance. Atoll Live and Naos Live provide the tools you need to detect, analyse, and automate the resolution of these issues, ensuring that your network operates at peak efficiency.

Ready to Optimize Your Network?

• Watch the replay of our webinar: [Optimising Radio Network Planning with Live Data].
• Download the slides to learn more about Atoll Live and Naos Live.
• Request a demo to see how these tools can transform your network planning.