AI in IMS Network: From Concept to Reality
Part 3 of Blog Series – Automation and AI is The Future – Are You Getting Left Behind?
In our previous blog, Key to AI Value Realization in Telecom, we explored why telcos must embrace AI and practical approaches to navigating a path to AI-driven network transformation. Now, we turn our focus to how Mavenir is bringing these concepts to life. In this blog, we will delve into how Mavenir is applying state-of-the-art AI/ML algorithms within the IP Multimedia Subsystem (IMS) network to enhance operational efficiency, reduce operational costs, and elevate the customer experience for Communication Service Providers (CSPs). Keep reading to discover the practical applications and tangible benefits of AI, brought by Mavenir, in the telecom industry.
To help mobile network operators easily implement AI in network operations – without worrying about hiring and training AI and data science experts to build telco-specific applications – Mavenir has developed an innovative framework called Network Intelligence as a Service (NIaaS). NIaaS is a holistic, AI-native, zero-code framework that leverages cloud-native architecture and an AI suite consisting of advanced machine learning (ML) algorithms – from Generative AI and Digital Twin to Deep Learning – that act upon live 4G/5G network traffic in real-time to tangibly boost operational efficiency, network performance, and customer experience.
The NIaaS framework also incorporates Mavenir’s Cloud-Native Automation capability to support the building of autonomous networks. By exploiting the declarative nature of cloud-native automation, the “current state” of the network can be advanced to the “desired state” as inferenced by the AI suite that is natively hosted in Mavenir’s NIaaS. Mavenir’s NIaaS and Cloud-Native Automation frameworks ingest AI-driven intelligence and declarative automation with K8s in live network operations of 4G/5G RAN and Core networks.
Check out our blog, Leveraging state-of-the-art automation capabilities in Telco, to learn more about the concept of declarative automation and how CSPs are successfully leveraging it through Mavenir’s Cloud-Native Automation to dramatically reduce time, cost, and errors in network deployment and operations. For this blog, let’s return to the AI use cases enabled by Mavenir’s Cloud-Native IMS and how they are helping leading CSPs to measurably improve operations efficiency, realize cost savings, and enhance customer experience.
AI Use Cases Enabled by Mavenir’s Cloud-Native IMS
- AI for IMS Network Capacity & Traffic Management
Many CSPs struggle with accurately assessing network capacity costs due to the limitations of traditional static methodologies, which fail to adapt to dynamic network conditions. AI-based capacity simulations offer a transformative solution, dynamically accounting for fluctuations in subscriber numbers, network functions (NFs), and temporary site outages. By leveraging AI models trained on diverse call patterns and real-world scenarios, these simulations provide precise resource predictions and valuable insights, especially during peak usage periods. This advanced approach enhances capacity planning accuracy and ensures optimal resource utilization – driving new levels of efficiency and cost-effectiveness in daily network operations.
Mavenir provides a sandbox environment for executing capacity simulations, ensuring CSPs’ planned changes can meet resource consumption thresholds. This environment simulates peak traffic conditions across cloud-native network functions (CNFs), offering critical insights into resource allocation. CSPs can easily assess if the deployment is over or under-provisioned, optimizing resources before taking nodes, pods, or sites out of service. Mavenir’s graphical user interface (GUI) delivers aggregated views at cluster, network function, and site levels, with customizable granularity windows for meticulous planning. It also offers visibility into traffic patterns (media, sessions, registrations) and performance-limiting factors.
Dynamic resource utilization leverages machine learning (ML) models to correlate resource consumption with specific network traffic. Mavenir’s GUI also facilitates “what if?” scenario exploration and provides flexible controls for adjusting CPU and memory thresholds. AI models can leverage this threshold information, along with subscriber numbers, to deliver precise pod estimates for deployment, aiding CSPs in scaling pods in or out. The explainability of AI models is crucial for implementing effective solutions, and Mavenir’s causation model enhances understanding by investigating the causes and constraints of different deployment scenarios.
Mavenir’s solution provides real-time visibility into network capacity. Machine learning models assess current traffic against a static call model, forecasting capacity utilization for subscribers and transactions per second (TPS). The system also proactively suggests measures to optimize resource allocation. The tool can identify specific pod types experiencing bottlenecks and recommend scaling measures to meet real-time capacity demands.
- AI for IMS Service Assurance
Existing service assurance tools monitor system health and identify anomalies through metrics and logs analysis, but often lack actionable intelligence for efficient issue resolution. Mavenir leverages machine learning to enhance IMS service assurance, expediting resolution times and improving overall operational management.
Mavenir’s IMS AI service assurance system employs AI agents that utilize a knowledge graph, encapsulating Mavenir’s extensive telecom infrastructure experience. This knowledge graph is accessible to operations teams, aiding more informed decision-making. Additionally, AI agents analyze detailed thread-level data and correlate it with Mavenir’s comprehensive operational knowledge repository.
This robust approach offers two key benefits: operations personnel gain valuable and timely insights into emerging issues and, consequently, are able to confidently compile and report necessary data to the R&D team. This significantly reduces resolution times and minimizes back-and-forth communication between operations and development teams.
AI agents can distinguish normal behavior from abnormal behavior, putting the knowledge gained from the knowledge graph into action. These agents are capable of analyzing data, identifying issues, and performing real-time pattern matching, effectively replacing human effort in IMS service assurance tasks. If an anomaly is detected, the agent requests more granular data from the IMS platform. In the case of an identified issue, an alarm is triggered, followed by a notification. The AI agent correlates these notifications with the knowledge graph to provide further insights into the issue. Additionally, the AI agent automatically initiates debug logs during events, making this information readily available to operations teams and saving critical time that would otherwise be spent on event analysis.
- AI for Voice Fraud Prevention
Mavenir’s CallShield – an AI-powered solution for voice-fraud prevention – offers remarkable enhancement of voice fraud prevention by leveraging advanced analytics and machine learning to detect and mitigate fraudulent activities in real-time. By monitoring real-time traffic data from IMS core network for voice interactions, CallShield can identify suspicious patterns and behaviors, such as anomalies in call durations, frequencies, and caller locations.
This real-time analysis allows for immediate action, preventing fraud before it causes significant damage and harm. Automated responses, such as blocking suspicious calls or alerting customers, further minimize the potential impact of fraudulent activities. The ability of the CallShield solution to continuously learn from new fraud patterns ensures that detection algorithms remain effective against evolving tactics, making it a powerful tool in maintaining the integrity of telecom services and improving customer trust. Mavenir’s solution also supports high capacity for anonymization of signaling and traffic data in high volumes: 100s of thousands transactions per second (TPS).
In this blog, we have looked at some of the main use cases of AI in the IMS network today, however, there are myriad exciting applications of AI for telcos – ranging from RAN to Core networks (including IMS).
As we wrap up this blog series on the transformative power of cloud-native automation and AI in the telecom industry, it’s evident that the future holds immense potential. By embracing these technologies now, CSPs can significantly boost operational efficiency, augment the customer experience, and fuel innovation to stay ahead in an ultra-competitive market. While this series has aimed to provide a comprehensive overview, the journey towards digital transformation is ongoing. We look forward to exploring more advancements and insights in future blogs but, in the meantime, one message is clear: now is the time for CSPs to take bold steps, invest in the right technologies, and build a resilient, future-ready network. Let’s continue to embrace the future together and unlock the full potential of the AI-powered telecom industry.
