AI automation for network performance
We have been working on our brand new unFIX EFFECTIVE Series for some time now. In this article we explore, a groundbreaking inspiration for us in the telecommunications industry, the significant role of artificial intelligence (AI) and machine learning (ML) in Ericsson’s approach to enhance network performance for communication service providers (CSPs). This approach focuses on automating network operations and optimizing customer experiences and emphasizes the need for a strategic approach to AI implementation to ensure maximum benefits and efficiency.
1. Introduction to AI in Network Performance: The potential of AI in transforming network operations, envisioning networks that can self-adjust coverage and capacity, heal themselves when issues arise, and optimize performance continuously without human intervention. With the emergence of 5G and new use cases like IoT, the demand for complex network infrastructures is increasing, necessitating efficient and automated network management.
2. Current State of AI Integration: While automation is currently the primary focus for operators, AI is recognized as a crucial tool for enhancing network performance. Traditional rule-based methods are still prevalent in telecom network automation, but there's a shift towards integrating AI algorithms into network components. These AI-based approaches aim to generalize across different network conditions, optimize performance, and minimize human intervention.
3. Incorporating AI into Network Components: Three main ways AI is being incorporated into network components:
- Replacing existing rule-based algorithms with AI-powered ones.
- Adding new AI-based components with enhanced functionality.
- Introducing AI-based control to existing legacy components.
These approaches have shown promise in boosting network performance and efficiency, particularly in fast control loops such as scheduling and link adaptation, as well as in slower control loops like network planning and optimization.
4. Evolution from Automation to Intelligence: Discern between standard automation and intelligent networks driven by AI. Four stages in the evolution towards AI-driven intelligence, starting from rule-based functionality to cognitive, intent-driven AI-based functionality. The ultimate goal is to have AI-controlled systems that optimize network performance based on high-level intent and business goals.
5. Performance Benefits of AI: Performance benefits observed through AI implementation, include increased spectral efficiency, improved downlink user throughput, and reduced transmission power. Examples from live networks demonstrate the effectiveness of reinforcement learning (RL) and digital twin simulations in optimizing network parameters and enhancing customer experiences.
6. Considerations for AI Implementation: Taking a holistic approach to AI implementation in network operations. It warns against focusing solely on individual use cases or isolated functions, emphasizing the need for broader coordination and conflict resolution within the network ecosystem. A strategic, end-to-end approach is necessary to fully realize the benefits of AI and ensure the efficiency of network automation projects.
In summary, it is obvious the transformative potential of AI in optimizing network performance for CSPs, from automating routine tasks to enabling intelligent, self-optimizing networks. However, the importance of a well-planned and coordinated approach to AI implementation is key to maximize its impact and efficiency.
To delve deeper into how AI can improve automation in your industry, explore our pragmatic unFIX EFFECTIVE Series here.
