Workloads
Generative AI
Industries
Telecommunications
Business Goal
Risk Mitigation
Products
NVIDIA AI Enterprise
NVIDIA NIM Microservices
NVIDIA NeMo
-
Overview
-
Technical Implementation
-
FAQ
Generative AI Accelerates Network Configuration, Deployment, and Operation
Telecom companies spent an estimated nearly $295 billion in capital expenditures (CapEx) and over $1 trillion in operating expenditures (OpEx) in 2024, including spending on manually based processes for network planning and maintenance. In a telecom network, configuration and optimization involve managing a vast number of interdependent parameters that directly affect network performance, user experience, and spectrum efficiency for millions of customers and end users. These settings need constant tuning from telecom network engineers based on time of day, user behavior, mobility, interference, and service types.
Generative AI, powering large telco models (LTMs) and AI agents enable the next generation of AI in network operations, supporting telecom companies to optimize their OpEx, use their CapEx efficiently, and unveil new opportunities for monetization. NVIDIA developed an agentic AI solution to bring autonomy into this dynamic environment by observing real-time network KPIs, making data-driven decisions, and automatically adjusting parameters.
Unlike traditional rule-based systems, an AI agent can perceive, reason through complex trade-offs, learn from feedback loops, and adapt to new conditions with human-in-the-loop feedback added as needed. It can also orchestrate changes across multiple layers and multiple vendors, enabling coordinated actions like load balancing, inter-cell interference coordination, or power saving in lightly loaded areas. This level of autonomous control not only improves efficiency and quality of service (QoS), but also reduces operational complexity and time-to-resolution for issues in dense, high-demand environments.
Quick Links
Getting Started With Generative AI for Network Operations
NVIDIA AI Blueprints enable scalable automation by providing a workflow that developers can use to create their own AI agents. With these, developers can build and deploy custom AI agents that can reason, plan, and take action to quickly analyze large amounts of data, summarize, and distill real-time insights.
The NVIDIA AI Blueprint for telecom network configuration delivers validated building blocks for network operations across multiple domains. This AI Blueprint enables developers, network engineers, telecom companies, and vendors to automate configuration of radio access network (RAN) parameters using an agentic LLM-driven framework.
Autonomous networks provide an opportunity to better manage OpEx. The AI Blueprint for telco network configuration facilitates this by providing a modular AI architecture and automation workflows needed for consistent, scalable deployments. Powered by generative AI, this AI Blueprint enables network engineers to add adaptive intelligence by predicting issues, optimizing performance, and automating decisions.
BubbleRAN and Telenor Adopt NVIDIA AI Blueprint for Telco Network Configuration
The AI Blueprint for telco network configuration is powered by BubbleRAN’s software on a cloud-native infrastructure which can be utilized for building autonomous networks at scale along with their multi-agent RAN intelligence platform.
Telenor Group, which serves over 200 million customers globally, plans to deploy the AI Blueprint for telco network configuration to address configuration challenges and enhance QoS during network installation.
Implementation Details
This agentic LLM-driven framework utilizes Llama 3.1-70B-Instruct as the foundational AI model, due to its robust performance in natural language understanding, reasoning, and tool calling.
Customers have the flexibility to deploy this Blueprint via:
- NVIDIA's hosted NIM™ microservices API endpoints at build.nvidia.com
- On-premises NIM microservices to meet privacy and latency requirements
End users interact through a Streamlit-based user interface (UI) to submit their queries or initiate network operations. These queries are processed by a LangGraph agentic framework, which orchestrates the specialized LLM agents.
The LLM agents are equipped with specialized tools that allow them to generate and execute SQL queries on both real-time and historical KPI data, calculate weighted average gains of the collected data, apply configuration changes, and handle the BubbleRAN environment.
We leverage prompt-tuning to inject contextual knowledge about the BubbleRAN network architecture, including the setup details and the interdependencies between various KPIs and the logic for balancing trade-offs to optimize weighted average gains.
The LangGraph-powered agentic framework orchestrates three specialized agents, each with distinct responsibilities that work together to close the loop of monitoring, configuration, and validation. Once the user initializes the network with selected parameters, they can choose between a monitoring session with a monitoring agent or directly query the configuration agent to understand parameter impacts and network status.
Quick Links
Below is a breakdown of each agent and their functionality:
1.Monitoring Agent
This agent continuously tracks the average weighted gain of preselected parameters in user-defined time intervals (default: 10 seconds) on a real-time BubbleRAN KPI database. When it detects performance degradation due to reduction in weighted average gain of a specific parameter, it raises the issue to the user for authorization of the next step.
2. Configuration Agent
The configuration agent can be activated by the monitoring agent’s hand-off or direct user queries about parameter optimization or network health. It analyzes historical data, then reasons through the analyzed trends and domain-specific knowledge of parameter interdependencies and trade-offs. Based on its analysis, it suggests improved parameter values to the user and waits for user confirmation.
3. Validation Agent
Once parameter adjustments are confirmed, the validation agent restarts the network with the new parameter configuration. It evaluates the updated parameters over a user-configurable validation period and calculates the resulting average weighted gain. If the real-time average weighted gain deteriorates further, it automatically rolls back to the previous stable configuration. Otherwise, it confirms success and updates the UI with the new settings.
In summary, our framework enables continuous, intelligent network optimization through an agentic loop, where specialized LLM agents work together to monitor, analyze, and validate parameter changes in real time. Equipped with tools to analyze real-time and historical KPI data, and with domain-specific knowledge of network parameters and trade-offs, these agents provide data-backed recommendations and explainable reasoning. This closed-loop design ensures that network performance remains autonomous yet user-controllable, empowering users to maintain optimal performance while retaining control on every decision point.
For more technical details, explore the Blueprint card.
Generative AI can analyze large volumes of data from equipment sensors to predict potential failures or issues. This helps technicians anticipate problems before they occur, allowing for timely maintenance and minimizing downtime.
Generative AI-driven analytics provide technicians with actionable insights and recommendations based on real-time data. This allows them to make informed decisions regarding repairs, upgrades, and network optimization.
Generative AI can automate repetitive and routine tasks, such as generating work orders, scheduling appointments, and creating documentation. This allows technicians to focus more on complex issues and customer service.
Quick Links
Optimize Network Operations With Generative AI
By leveraging NVIDIA AI, telecommunications companies can reduce network downtime, increase field technician productivity, and deliver better quality of service to customers. Get started by reaching out to our team of experts or exploring additional resources.
