• +42% USER THROUGHPUT
• -39% CALL DROP RATE
• -61% HANDOVER FAILURES
• -76% POOR QUALITY CALLS

• +21% DATA RATE
• +14% SPECTRAL EFFICIENCY
• -19% CUSTOMER CHURN
• -80% DRIVE-TEST EFFORT

• USD 4M OPEX REDUCTION
• USD 19.25M CAPEX REDUCTION
• USD 13.5M RETAINED REVENUE
• USD 36.75M OVERALL EFFECT

The solution unifies measurements, topology and performance data across heterogeneous RAN environments within a common analytics framework.

Topology parsing and data processing support Nokia, Ericsson and Huawei RAN environments, enabling a consistent optimization methodology across multi-vendor networks.

Apply the same issue detection, root cause analysis, prioritization, and optimization workflow across the entire RAN using a common analytics and optimization framework.

Measurement density increases dramatically from conventional drive testing to MDT and, where available, analysis of all traffic.

Continuous measurements extend visibility beyond predefined drive-test routes and locations accessible to field teams.

Geolocated measurements reveal where problems occur, how subscribers are affected and which areas should be optimized first.

Understand what is happening in your network based on KPIs, traffic profiles, and QoE metrics

Identify exactly where network issues occur and how they affect subscribers

Determine why it's happening with AI/ML and Correlation with Network topology

Identify and geolocate complex network problems, including interference, overshooting, undershooting, cross-feeder, cross-MIMO, and topology-related issues.

Rank detected problems according to traffic impact, affected subscribers, expected network impact and implementation effort.

Generate targeted optimization actions including antenna tilt, transmit power, azimuth, PCI, cabling and configuration corrections.

Combine MDT and L3 measurements with PM/CM data, topology, traffic, QoE, capacity requirements and financial inputs to establish the technical and commercial baseline for network evolution planning.

Model alternative actions such as optimizing existing sites, modernizing or upgrading infrastructure, and adding new sites to understand how each scenario could affect performance and capacity.

Rank candidate actions using actual demand, QoE impact, capacity requirements, urgency and expected technical benefit to identify which sites, clusters and network changes should be addressed first.

Increase spectral efficiency and utilize deployed network assets more effectively, enabling additional capacity without immediately expanding physical infrastructure.

Identify and resolve coverage, throughput, call-quality and mobility problems based on actual network measurements.

Reduce dependence on conventional field measurements and accelerate troubleshooting, problem localization and network optimization.

• Predefined routes
• Limited time windows
• Outdoor-biased visibility
• Manual field effort
• Lower sample density

• Continuous live-network measurements
• Indoor and outdoor visibility
• Hard-to-reach locations covered
• Much higher data density
• Faster issue localization

• Issues resolved
• Balanced coverage and capacity
• Consistent QoE across the area
• Optimized network performance