Ericsson's AI Value in Radio Access Network (RAN)
Executive Summary
- AI Integration: AI plays a pivotal role in enhancing the capabilities of radio access networks (RAN), focusing on radio functions, predictions, and control loops.
- Market Growth: Forecasted to significantly expand, with AI in telecom set to grow from $2.2 billion in 2023 to $19.5 billion by 2030.
- Challenges & Solutions: Addressing challenges like real-time processing and rapid solution verification, AI initially targeted slower control loops, later expanding to faster loops for greater gains.
- Ericsson's Role: Ericsson has successfully deployed AI in RAN through features like AI MIMO Sleep and Link Adaptation, delivering substantial energy savings and throughput improvements.
Key Findings
AI in RAN Journey
- Initial Phase: AI integration began with 4G, evolving with 5G technologies.
- New Architecture: A distinct AI-driven architecture is proposed for RAN, emphasizing specialized knowledge in radio network data and software.
- AI Feature Phases: AI progression in RAN is divided into rule-based, AI-powered, AI-native, and intent-driven stages.
Opportunities in AI for RAN
- Enhanced Performance: AI delivers improved network performance, efficiency, reliability, and resource management.
- Innovative Features: Examples include AI MIMO Sleep and Link Adaptation, showcasing significant energy savings and throughput improvements.
AI Adoption Challenges and Success Factors
- Data Operations Understanding: Requires deep understanding of data operations and skilled development teams.
- Network Complexity Reduction: AI helps manage growing network complexities and supports cost-effective performance.
Ericsson's AI Strategy
- AI Integration in RAN: Ericsson’s AI journey started with augmenting rule-based features with data and ML algorithms, leading to high-performing AI-powered features.
- Advanced AI Features: Transitioning to AI native features where AI is integral to design and development, paving the way for intent-driven and AI-native RAN.
- Generative AI Potential: Emerging as a disruptive technology, generative AI is anticipated to play a significant role in enhancing AI in RAN journeys.
AI in RAN Impact
- Performance and Sustainability: AI contributes to both performance improvements and sustainability in telecom networks.
- Advanced Radios and Programmable Networks: Utilizing advanced radio technologies and programmable network architectures to enhance AI capabilities.
- Intent-driven Operations: Shifting towards networks that are adaptable and optimized for specific user experiences and energy efficiency goals.
Strategic Areas for AI in RAN
- New 5G Use Cases: Exploiting the full potential of 5G through AI.
- Data Management at Scale: Leveraging big data for better decision-making.
- Generative AI and Open Standards: Embracing innovative technologies and fostering ecosystem collaboration.
- Multivendor Solutions: Supporting diverse vendor ecosystems while maintaining AI integration.
Conclusion
Ericsson's AI in RAN strategy demonstrates a comprehensive approach to leveraging AI for optimizing network performance, enhancing user experience, and driving sustainable growth in the telecommunications sector. Through strategic AI implementations and continuous innovation, Ericsson is positioning itself as a leader in advancing AI capabilities within radio access networks.
