Cognitive Software
Every radio access network (RAN) has different planning and optimisation issues, regardless of the network architecture or generation. In order to quickly react to changing use cases and accelerate time to market, Ericsson’s Cognitive Software network planning and optimisation solution uses a cutting-edge AI-based methodology to optimise network performance. The company is also investigating Google Cloud.
It is a difficult effort to find and fix network problems when there are hundreds of thousands of cells in a network to evaluate. Nonetheless, cognitive software’s robust history of AI-driven network optimisation offers clever ways to identify and fix anomalies in the network.
Google Cloud and Ericsson Cognitive Software have worked together throughout technology research to combine cutting-edge hyperscale cloud services like BigQuery and Vertex AI. Due to this integration, Google Cloud’s Vertex AI was used to demonstrate the Cell Anomaly Detector use case, which was initially introduced at the Mobile World Congress in Barcelona in 2024.
The demonstration shows off the potential of utilising the Cognitive software AI model from Ericsson, which is installed on Google Cloud’s Vertex AI, to identify anomalies in cellular networks. This is an interesting development at the nexus of cloud computing and digital technologies.
The Google Cloud and Ericsson investigation uses AI technology to further improve network design, optimisation, and operation while offering operators dynamic, scalable solutions that drastically shorten time to market.
An innovative approach to network performance management is the Cell Anomaly Detector
Ericsson’s Cognitive Software created the Cell Anomaly Detector in order to proactively detect, categorise, and resolve cell-related problems in radio access networks (RAN).
This use case, which performs a multi-dimensional analysis on more than 200 KPIs to find hidden patterns and swiftly and reliably identify problems, is a pathfinder in the telecom industry. With an astounding 98% accuracy rate, the tool is able to classify aberrant cells into multiple issue classes, maybe surpassing human skill levels.
Following that, a web user interface with APIs to integrate with other apps already in use by communication service providers and comprehensive insights into the problems are presented. For more than 60 network operators worldwide, this strategy has significantly improved network KPIs, decreased customer complaints, and minimised operational cost (OPEX).
The importance of hyperscale cloud providers in enhancing Ericsson’s capabilities with Cognitive Software
A key to success in the ever changing tech scene of today is having domain expertise. Utilising our industry-leading RAN domain experience combined with cutting-edge AI technology to fully realise the potential of next-generation networks, Ericsson provides its Cognitive Software. In addition, Google cloud investigate the advantages of server-less services using Google Cloud, which enable CSPs (communication service providers) to maximise their total cost of ownership (TCO).
This is the situation in which an HCP (hyperscale cloud provider) structure becomes useful. Utilising services from a source such as Google Cloud allows us to reduce many of these expenses, increase consumer value, and spur innovation.
MLOps’s contribution to enhancing Ericsson’s Cognitive Software
Building, deploying, and operationalizing machine learning systems quickly and reliably is made possible by Machine Learning Operations, or MLOps, which offers a standardised set of procedures and technological capabilities. This methodology is essentially a machine learning and data science extension of DevOps.
They can increase the effectiveness, scalability, and reliability of Google Cloud products by using MLOps. Parts of the machine learning process can be automated, which can improve results and save expenses. Data from RAN performance management is ingested and aggregated to KPIs kept in Google Cloud’s BigQuery for the Cell Anomaly Detector. The VertexAI MLOps platform then processes this data, sending the conclusions to cloud storage.
Google Cloud solutions like BigQuery and Vertex AI offer serverless Software as a Service (SaaS) features that can lower TCO compared to IaaS. The SaaS approach lets you pay as you use the services, not in advance.
The Cell Anomaly Detector Demonstration
Leader in telecom infrastructure, Ericsson, is transforming its Cognitive Network Solutions by using the potential of Google Cloud. These AI-powered technologies are designed to provide outstanding user experiences, lower costs, and maximise network efficiency. By working together, these titans of industry are expanding the limits of what is feasible in the field of network administration.
AI Engine: Cognitive Software
Ericsson’s Cognitive Network Solutions are based on Cognitive Software. This software suite automates network operations that have historically been performed by human engineers by utilising artificial intelligence (AI). Envision a network consisting of hundreds of thousands of cells, all of which need continuous optimisation and monitoring. By evaluating enormous volumes of network data, seeing trends, and automatically modifying network settings for optimal performance, Cognitive Software overcomes this difficulty.
The Benefits of Google Cloud
Ericsson realised that in order to fully utilise Cognitive Software, which is a potent instrument, a stable cloud platform was necessary. Here’s where Google Cloud can help. Google Cloud has numerous significant benefits:
Scalability
Demands on telecom networks change during the course of the day. Because of Google Cloud’s highly scalable infrastructure, Cognitive Software can adjust to these changes and maintain the resources it requires to operate at its best.
Machine Learning Expertise
Google possesses a leading position in machine learning (ML) technology. Ericsson may take advantage of Google Cloud’s Vertex AI platform to better improve the capabilities of its AI models inside of Cognitive Software. Vertex AI accelerates innovation and enhances network insights by streamlining the creation and implementation of machine learning models.
Data analytics
The ability to analyse large amounts of data is essential for effective network optimisation. A strong foundation for storing and analysing network data is offered by Google Cloud’s BigQuery service. Ericsson network behaviour thanks to BigQuery, which helps Cognitive Software make wiser choices.
A Joint Venture: The Cell Anomaly Detector
The Cell Anomaly Detector is a perfect illustration of how Ericsson and Google Cloud collaborated. This AI-powered programme finds anomalies in cellular networks by using Vertex AI. The Cell Anomaly Detector uses real-time data analysis to identify possible problems such as congestion or signal interference before they have a major negative influence on the user experience. Service providers may quickly resolve issues with this proactive approach to network management, reducing downtime and guaranteeing a positive customer experience.
The Prospects for Network Management
Network management has advanced significantly as a result of the partnership between Ericsson and Google Cloud. Through the application of AI and cloud computing, Ericsson is developing a new class of cognitive networks that include:
Self-Optimizing
Networks that are self-optimizing may adapt to shifting demands on their own and maintain peak performance without the need for human intervention.
Predictive
Predictive maintenance and increased network stability are made possible by AI’s capacity to foresee possible problems with networks before they arise.
Economical
By streamlining resource allocation and automating network chores, service providers can save a lot of money.
With better reliability, faster speeds, and a more seamless user experience, this technical revolution promises to completely transform the way we experience mobile connectivity.
In summary
The implementation of HCP and MLOps in conjunction with Ericsson’s Cognitive Software has been shown to have potential through technical research conducted using Google Cloud. The Vertex AI framework’s complete automation of ML model life cycle management guarantees stable, scalable, and adaptable operations. It also simplifies ML model maintenance, identifies accuracy deviations, speeds up time to market, and above alllowers total cost of ownership (TCO) thanks to HCP’s pay-as-you-go consumption model.
