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rApps for best user experience

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User experience

User demands and growing complexity make network performance more vital than ever. Intelligent automation using rApps has the potential to revolutionize the world of network management and orchestration, augmenting or replacing existing manual processes in order to improve network performance and reduce costs.

The challenge

The latest Ericsson Mobility Report predicts that there will be 5.6 billion 5G subscriptions worldwide by 2029 and as a consequence of that, networks complexity will keep increasing.

Unfortunately, today’s network performance management and automation solutions are not well adapted to guarantee an optimal level of service and flawless user experience for this many subscriptions in complex networks. 

To cost-effectively detect network issues and maintain optimal service at scale, AI-powered solutions have become a must and that is where rApps come in.

rApps enable shift to autonomous networks

How rApps improve network performance

Network performance is often optimized by combining manual and automated rule-based instructions with predetermined thresholds for each network performance metric. 

As the network complexity increases, the number of these rules increases which makes manually maintaining these algorithms a highly complex task. Anomaly Detection rApps are a proven AI powered software solution. Over the last few years, these algorithms were deployed as part of the Cognitive Software solution and have demonstrated the possibility of creating a fully scalable and automated AI powered solution for network optimization.

This solution enables a paradigm shift. The issue-centric focus will simplify the process and fix a higher amount of malfunctioning network elements by similar actions, complementing traditional worst cell list and elevating the network performance to a superior level.

Leverage the Open RAN SMO

How AI algorithms take rApps to another level

AI in rApps isn’t just an upgrade—it’s a game-changer. Get expert insights into the three key AI techniques: Graph Neural Networks, Reinforcement learning and AI Explainability. 
These models make networks smarter, more autonomous, and future-ready. Our expert Wenfeng explains how we bring these innovations into real-world production. 

How AI algorithms take rApps to another level

How these rApps leverage the Open RAN SMO

The rApps run in an Open RAN SMO platform. This platform can manage rApps in a multivendor network environment, and allows to use other rApps from the Ericsson, CSPs or even other companies. 

Anomaly detection rApps, deployed on a SMO, will collect data from the any type of network, detect abnormal network behaviours, and propose changes. The platform will also provide conflict management to avoid executing contradicting proposals from different rApps, and will always implement the solution with highest positive impact.

This will make the solution fully adapted to the needs, and all optimization tasks more efficient.

How these rApps leverage the Open RAN SMO

Why are rApps better

Using rApps, communication service providers will be able to overcome a wide variety of network management challenges, delivering significant benefits in terms of operational cost, network service performance and resource utilization. Compared to legacy automations, rApps are more flexible, agile, and scalable, and will transform the way we supervise and optimize our networks.

Realizing the full vision will, however, require a deliberative and stepwise approach. Ericsson’s comprehensive strategy for the use of rApps is based on our deep understanding of the challenges posed by heterogeneous automation use cases running simultaneously and on our years at the forefront of the development of an open automation platform architecture.

rApp examples

Improving network performance is all about finding suboptimal configurations, and the anomaly detection rApp identifies them more effectively than ever before.
Unlike today’s tools, it doesn’t just compare cell performance to a set of pre-defined KPIs.

It constantly runs network-wide pattern analyses and uses AI to detect cells that are acting differently, revealing issues before they even start causing degradation.

It’s this scale and speed that’s helping providers continuously improve their networks — and move towards closed-loop performance optimization.

Anomaly detector rApp | Uplink anomaly detector rApp | Anomaly root cause explainer | Voice connection optimizer rApps

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