Intent-Based Energy Saving: A New Paradigm for 5G Network Management

The telecommunications industry is at a pivotal juncture. The rollout of 5G and the anticipated development of 5G-Advanced networks promise unprecedented speed, capacity, and connectivity, fueling innovation across various sectors. But this progress comes with a significant challenge: a dramatic increase in energy consumption. As mobile networks become denser and more complex, the energy required to power them threatens to overshadow the benefits they deliver, both in terms of operational costs and environmental impact.

Traditional approaches to energy saving in telecom networks have often relied on static, pre-configured rules and thresholds. While these methods offer some level of optimization, they often fall short of maximizing energy efficiency. They struggle to adapt to the dynamic nature of 5G traffic, which fluctuates wildly depending on time of day, user behavior, and location. This rigidity can lead to either wasted energy during periods of low demand or compromised network performance during peak times.

This is where intent-based energy saving emerges as a game-changer, heralding a new paradigm for 5G network management. It represents a shift from reactive, rule-based optimization to a proactive, intelligent approach that leverages the power of Artificial Intelligence (AI) and Machine Learning (ML) to achieve unprecedented levels of energy efficiency without sacrificing user experience.

Understanding the “Intent” in Intent-Based Networking

At its core, intent-based networking (IBN) is about translating high-level business objectives or “intents” into automated network configurations and actions. In the context of energy saving, the “intent” could be as simple as “minimize energy consumption during off-peak hours” or as complex as “reduce energy consumption by 10% while maintaining a specific quality of service for high-priority users.”

Instead of manually configuring intricate rules for each base station, operators can express their desired outcomes in a natural, declarative way. The IBN system, powered by AI, then takes over, automatically analyzing the network, identifying the optimal strategies, and implementing the necessary changes to achieve the stated intent.

How AI Fuels Intent-Based Energy Saving

The recent collaboration between China Mobile Communications Group and Nokia, culminating in China Mobile’s procurement of Nokia’s 4G/5G base station AI intelligent energy-saving platform software on December 26, 2024, provides a compelling real-world example of this technology in action. Nokia’s solution, guided by the IMT-2020 (5G) Promotion Group, has undergone rigorous testing, demonstrating the significant potential of AI-driven energy optimization.

Here’s a breakdown of how AI plays a crucial role in enabling intent-based energy saving within this context:

1. Data Collection and Analysis: AI algorithms ingest vast amounts of data from various sources within the network, including base station performance metrics, traffic patterns, user behavior, and even environmental factors. This data provides a holistic view of the network’s dynamic state. For example, Nokia Bell’s EMP energy management platform can autonomously analyze different ranges of rate distributions in the network. The platform can identify which users or areas are experiencing high or low data rates. This information is crucial for tailoring energy-saving strategies.
2. Predictive Modeling: Using ML techniques, the AI engine builds predictive models that forecast future network behavior. Nokia’s solution, for instance, can predict network indicators one day in advance based on historical data. This foresight allows the system to anticipate periods of low and high demand, enabling proactive adjustments to energy-saving parameters. For example, using historical data, the system might predict that traffic will be significantly lower between 2 AM and 5 AM. Based on this prediction, it can adjust energy-saving thresholds for those hours.
3. Intelligent Strategy Selection: Based on the operator’s defined intent and the predicted network conditions, the AI engine intelligently selects and applies the most appropriate energy-saving strategies. This could involve dynamically adjusting the operating parameters of base station equipment, such as power output, or even temporarily switching off certain cells during periods of extremely low demand. Nokia’s EMP platform, for instance, can intelligently select and control energy-saving strategies based on the operator’s intent.
4. Automated Implementation: The chosen strategies are automatically implemented across the network, eliminating the need for manual intervention. This automation ensures that the network is constantly adapting to changing conditions in real time. For instance, the system can automatically adjust the power output of base stations or switch off certain cells during periods of low demand.
5. Continuous Monitoring and Optimization: The AI engine continuously monitors the network’s performance and the effectiveness of the applied strategies. It uses this feedback loop to refine its models and further optimize energy savings over time, learning from past successes and failures. The system continuously monitors the network’s performance to ensure that energy-saving measures are not impacting user experience. It can adjust strategies in real time if needed.

The Benefits of Intent-Based Energy Saving: A Win-Win for Operators and the Environment

The advantages of this intelligent approach are multifaceted:

• Maximized Energy Savings: By dynamically adapting to real-time network conditions, intent-based systems can achieve significantly higher energy savings compared to static methods. The tests conducted by Nokia, which demonstrated a 10.5% reduction in average energy consumption during off-peak hours and a 10.1% reduction with intent-driven saving, are a testament to this potential.
• Enhanced Network Performance: Unlike rigid rule-based systems that can sometimes compromise performance, AI-powered solutions prioritize maintaining a satisfactory user experience. They carefully balance energy savings with the need to meet service level agreements (SLAs) and ensure seamless connectivity. In the multi-scenario multi-base station environment of the existing network, AI optimization brings more than 10% energy saving gain and 2.3 times increase in energy saving time compared with static policy configuration, without affecting the service index of the network.
• Reduced Operational Costs: Lower energy consumption directly translates into reduced operational expenses for telecom operators, freeing up resources for other strategic investments.
• Environmental Sustainability: By minimizing energy waste, intent-based energy saving contributes to a smaller carbon footprint for the telecommunications industry, aligning with global efforts to combat climate change. This action also demonstrates China Mobile’s commitment to the national strategic goals of carbon peaking and carbon neutrality.
• Future-Proofing the Network: As 5G networks evolve and become even more complex, the ability to intelligently manage energy consumption will be increasingly critical. Intent-based systems provide a scalable and adaptable solution that can evolve alongside the network.

“Energy Twins”: The Next Frontier in Network Optimization

Nokia’s innovative use of “Energy Twins” further enhances the capabilities of its energy-saving platform. This concept involves creating a virtual replica or simulation model of the physical network. By feeding real-time data into this digital twin, the system can simulate the impact of different energy-saving strategies without affecting the live network.

This allows for risk-free experimentation and optimization, enabling the identification of the most effective strategies before they are deployed in the real world. The “Energy Twins” model acts as a sandbox for testing and refining energy-saving strategies, ensuring optimal performance and minimizing potential disruptions.

Challenges and Considerations

While the potential of intent-based energy saving is immense, it’s important to acknowledge the challenges that need to be addressed for widespread adoption:

• Data Privacy and Security: The reliance on vast amounts of data raises concerns about privacy and security. Robust data governance frameworks and anonymization techniques are essential to protect sensitive information.
• Algorithm Transparency and Explainability: Understanding how AI algorithms arrive at their decisions is crucial for building trust and ensuring accountability. Research into explainable AI (XAI) is vital for demystifying the “black box” of AI decision-making.
• Initial Investment and Integration: Implementing intent-based systems requires an upfront investment in AI infrastructure and integration with existing network management systems. Operators need to carefully evaluate the return on investment and plan for a smooth transition.
• Standardization and Interoperability: Industry-wide standards and protocols are needed to ensure interoperability between different vendors’ IBN solutions and seamless integration with diverse network equipment.

The Path Forward: Embracing the AI-Powered Future of Telecom

China Mobile’s strategic move to adopt Nokia’s AI-powered energy-saving platform signifies a major step towards a more sustainable and efficient future for the telecommunications industry. It serves as a powerful example for other operators worldwide, demonstrating the tangible benefits of embracing intent-based networking principles.

As AI technology continues to mature and the challenges are addressed, intent-based energy saving is poised to become the new standard for 5G network management. By embracing this paradigm shift, telecom operators can unlock significant energy savings, reduce their environmental impact, and pave the way for a truly connected and sustainable future. The journey towards “zero emission, zero contact, zero failure” networks has begun, and AI is undoubtedly the driving force behind this transformative journey.