In the rapidly evolving world of networking, the advent of network digital twins has brought about a paradigm shift. This innovative approach enables telcos to accurately replicate network conditions in a virtual environment and leverage advanced analytical models to simulate and analyze network ecosystem performance. One of the hallmarks of network digital twins is their ability to provide decision intelligence. Previous versions of AI automation allowed for data collection, but lacked fine-tuning. This resulted in several gaps and blind spots. Network digital twins have proven to be a game-changer in that they provide a holistic, higher-level view and help add context to decision-making. Network optimization processes that once existed in silos can now be integrated and centralized, resulting in significant cost savings and increased ROI.
About network digital twins
Network digital twins leverage self-learning AI models and advanced analytics to capture real-world network entities and processes in real-time. Network digital twins provide insight into a 5G network ecosystem, including its health and performance, by collecting data from multiple sources, such as service state changes, configuration changes, sensor data related to network traffic, and service fulfillment and assurance. Provides a comprehensive view. This provides valuable insight for decision-making in optimizing network operations, identifying inefficiencies, and improving overall network efficiency.
Through multi-layer AI closed-loop automation, telecommunications companies (telcos) and communications service providers (CSPs) can achieve zero-touch operations, unified infrastructure, and streamlined warranty processes. Network digital twins can leverage domain- or cross-domain-based large-scale language models (LLMs) to analyze failures and incidents, identify root cause and impact, and apply automated AI-based solutions. It can also update and improve itself through real-time data, simulation, and intent-based networking (IBN).
Advantages of implementing a network digital twin
Implementing a network digital twin provides significant opportunities for sustainable business innovation, including:
- Enhanced network planning:
Visualizing and simulating your entire 5G network before deploying it into your network infrastructure allows for better optimization, reduced risk, and increased productivity. - proactive maintenance:
By leveraging real-time data fed into a network digital twin, operators can predict and prevent network failures and significantly reduce downtime. This approach enables proactive maintenance, self-assist, and self-recovery capabilities to improve the customer experience. - Enhanced network intelligence:
Network digital twins leverage AI and data analytics to transform network data into valuable insights, enabling informed decision-making, anomaly detection, and automated network optimization. - Improved resource efficiency:
Network digital twins give operators detailed visibility into network usage and traffic patterns. This knowledge allows you to optimize resource allocation, leading to lower costs, increased accuracy, and improved performance. - Strengthening compliance:
Dynamic compliance with service level agreements (SLAs) enables efficient and accurate monitoring of network performance and compliance with quality standards. Helps resolve potential issues and violations and minimize downtime and service interruptions.
Implementing a network digital twin facilitates network configuration changes, resulting in a better customer experience. Operators can also use insights generated by network digital twins to more effectively plan and execute network growth strategies and accelerate 5G deployment.
Ensuring security and privacy in network digital twins
Ensuring the security and privacy of network digital twins is critical because they rely heavily on data and real-time updates. Operators must comply with data privacy regulations and implement strict data access controls to protect sensitive customer information.
AI systems also rely on training data for accurate predictions and decisions. However, there is always a risk of malicious attacks and data manipulation on AI models. Ensuring the integrity and security of your AI training data is critical to protecting against malware and maintaining the effectiveness of your AI models.
To address this issue, organizations must implement robust security measures to protect training data. This includes the use of encryption techniques, access controls, and secure storage systems to prevent unauthorized access or tampering. Additionally, regularly monitoring and auditing your data can help detect potential attacks and anomalies.
Wipro’s approach to network digital twins
Wipro has successfully implemented network digital twin use cases on Nvidia Omniverse and Nvidia TPU platforms. These network digital twins are built on a robust layered architecture that effectively leverages real-time and historical trend data from the physical network environment. The Nvidia platform is used to generate comprehensive 3D-based models, which help calculate multimodal predictions. Wipro’s AI-based foundation library for network automation works with the Nvidia platform to help multiple network use cases evolve, meet business requirements, enable acceleration of virtualization, and comply with market standards. We provide a fulfilling environment.
Get ready for 6G and beyond
Looking to the future, network digital twins will become increasingly important in the development of 6G and beyond. These serve as the foundation for integrating various domains within the telecommunications industry, such as wireless networks, cloud infrastructure, and IoT devices. By leveraging AI and machine learning, these digital twins enable seamless communication and coordination between network components, ultimately improving connectivity and user experience.
Companies like Wipro are recognizing the importance of network digital twins in leveraging the full potential of 5G. As we move closer to 6G, these models will become even more sophisticated, enabling operators to unlock new possibilities and drive the next wave of innovation in automation and artificial intelligence.
