Opanga Networks

MERLIN

MERLINTM

Unlock in-depth network business intelligence

PEREGRINE

MERLIN

JOULE ML

RAN Business Intelligence platform

CAPEX efficiency has become increasingly important for Mobile Operators as traffic volumes and energy costs soar and interest rates reaching new highs.

Merlin RAN Business Intelligence platform delivers deep financial insight into RAN investments, using CX KPIs; enabling operators to materially improve their financials while maintaining network quality.

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Key insights to drive business
decisions
At the heart of Merlin is the RANStatsTM data lake which views RAN performance from a consumer application standpoint. Every time a user communicates on the mobile network, Merlin records critical CX performance metrics for that data session, alongside application-level information. These CX KPIs are then stored together with detailed radio channel information and are available for post-processing using advanced ML Applications. For the first time in the wireless industry, Merlin connects CX performance directly with cell site realities.
  • Understand which specific cell sites are not meeting performance targets
  • Optimize individual cell site performance to contribute to the larger business
  • Optimize individual cell site performance to contribute to the larger business
  • Prevent the defection of users identified as likely to leave your network
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With billions of dollars of CAPEX in play, 4G/5G demands modernized RAN Business Intelligence

For mobile operators, real-time RAN control is the most valuable untapped opportunity for ML application development. Using Opanga’s proven ML-enabled data lake, Merlin combines network-wide data with second-by-second RAN insights.

RANStats data lake is built by disaggregating control protocol and traffic information:

  • Inference of real-time RAN conditions
  • Automatic performance triggers initiate second-by-second data collection reported every minute
  • Data samples include all key subscriber, application, and network information for ML deep learning
  • Supervised data models train quickly with high levels of accuracy as a result of dynamic data granularity