Mavenir extends AI and analytics portfolio to enable optimization, automation and security of mobile networks

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Mavenir extends AI and analytics portfolio to enable optimization, automation and security of mobile networks

Mavenir, the industry’s only end-to-end cloud native network software provider and a leader in accelerating software network transformation for communication service providers (Communication Service Providers , CSPs), today announced its extended portfolio of artificial intelligence (AI) and analytics to enable closed-loop automation and drive digital transformation.

Mavenir’s AI / ML-based security and anti-fraud solutions are taking care of Telefónica Argentina’s continued revenue savings”

AI and machine learning (ML) in the mobile network infrastructure are expected to reduce costs by automating functions that normally require human interaction, and to accelerate new revenue-generating service offerings, becoming increasingly important as open radio access networks (Open RAN) and 5G cores are deployed.

Mavenir’s AI and analytics portfolio includes solutions designed to analyze and derive inferences from large amounts of unstructured data to automate networks, achieve cost savings and build 5G use cases. Many Industry 4.0 use cases, such as Intelligent Video Analytics and AR / VR, are powered by 5G which require AI-based inferences at the tip. Mavenir’s portfolio includes these AI-enabled applications for network automation, intelligent operations, EdgeAI and network security.

  1. Network Automation: Mavenir’s RAN Intelligent Controller (RIC) and Network Data Analytics Function (NWDAF) follow the specifications introduced by the O-RAN Alliance and 3GPP and operate at the heart of a network automation vision. RIC and NWDAF allow the network to dynamically adapt to traffic conditions, using machine learning (ML) based algorithms and applications that can be deployed on any network in a multi-vendor environment. Mavenir’s containerized RIC and NWDAF product features include:
  •    Non-RT RIC: designed to host advanced ML algorithms (rApps) to optimize network performance and train ML models using long-term RAN data for dynamic and adaptive policies to optimize RAN performance.
  •    Near-RT RIC: designed to host trained AI / ML models (xApps) to infer and control functional O-RAN elements in near real time.
  •    NWDAF: designed to…