Telco interest in automating mobile network operations is growing as early efforts result in streamlined processes, better customer service and reduced costs. However, reaching the ultimate automation goal of network autonomy can take several years and may seem overwhelming without a framework and a knowledge of AI-based tools that can help.
To help its operator customers achieve higher levels of network automation, Rakuten Symphony and Rakuten Mobile are working together to document 25 AI use cases and tools that can inspire mobile operators as they engage in their own automation efforts.
We aligned these use cases with the TM Forum’s six-stage network taxonomy to provide structure for operators that are moving toward network autonomy. This taxonomy starts at level zero (no automation) and goes to level five (fully autonomous). These AI use cases have helped the Rakuten Mobile network to get to network autonomy level 3.5 – between conditional automation and AI assisted automation.
We’ve taken snippets of the best posts on AI use cases and curated them here for easy access.
With the growing popularity of Open RAN, telco cloud and virtualization initiatives, service providers have an increased need to manage software development life cycles (SDLC) for a wide range of network functions. Rakuten Symphony’s AI-based SDLC software (AI-SDLC) is a chat-based generative AI module that helps users perform the full range of SDLC tasks, starting with business case development and finishing with code quality assurance and all processes in between.
The tool can be used by product managers, business analysts, user interface/experience designers, software developers, quality assurance and release managers who contribute to the SDLC process. The SDLC tool reduces the time and cost of SDLC by providing real-time responses to data inputs, immediately notifying team members of errors and correcting them. With this AI-based tool, there is greater process consistency due to 95% accuracy in generating SDLC artifacts for engineering, as well as a 40% increase in process efficiency when using the tool.
Capacity management for MNOs ensures the network infrastructure has capacity to handle data throughput for today’s users and has the scalability to handle expanded future workloads. Without an AI model, network engineers use a rule-based approach to capacity where alarms are triggered when the network capacity needs adjustment. This approach is not very accurate and can lead to capacity issues that persist until an engineer can analyze the issue and respond.
The Rakuten Symphony Capacity Forecasting AI Model predicts future traffic, users, physical resource blocks (PRB) and capacity using machine learning, historical data and deep learning algorithms.
Rakuten Symphony’s Network Performance Anomaly Detection use case uses AI/ML to automate the monitoring of network performance KPIs to identify unusual patterns in the performance. Many MNOs still identify network problems manually, which is time-consuming and has a long issue resolution time. Now, MNOs can use these KPIs as detectors to identify and catch problems early and help improve quality of services.
At the start of site construction, the field engineer is responsible for receiving the technology and materials – from construction materials to equipment cabinets, to RAN systems and more. All of this equipment must be accounted for to keep the build out on track and also added to inventory so that the MNO has a record of its equipment.
The Site Selection Image Recognition use case brings order to this complex process by automating the process using the Rakuten Site Manager solution suite with AI enhancements. With Rakuten Site Manager, the field engineer has a tool to complete their equipment installation task/checklist and upload the pictures of the installed equipment as proof. This checklist will be later submitted to the supervisor who will verify it based on the pictures attached to determine whether or not the installation, checklist/test case is correct.
Building out a cell site is a complex endeavor with highly detailed processes, critical dependencies, multiple vendors, deadlines, equipment and more. Managing the build out and ongoing operation of these sites is a great challenge for process mining AI initiative that is built into our Rakuten Site Manager software.
With Rakuten Site Manager’s AI capability, users can gain a detailed overview of the site project’s performance, pinpoint areas for improvement, and make informed decisions to ensure the project is completed successfully and on time. It aims to guarantee a 100% completion rate within the designated timeframe, with proactive notifications about program progress and timely resolution of any issues that arise during implementation.
Interested in more use cases that can help your network? See how Rakuten AI for Telecom can help your network become more autonomous.
