As communications service providers (CSPs) navigate the complexities of mid-5G deployment, artificial intelligence (AI) emerges as a pivotal technology to enhance energy efficiency in 5G Radio Access Networks (RAN). CSPs are under increasing pressure to reduce operational costs and achieve sustainability goals. AI’s potential to optimize RAN operations aligns both short-term cost reduction objectives and long-term sustainability targets. By strategically utilizing AI, CSPs can maximize their existing network capabilities while investing in future-proof technologies, ultimately supporting initiatives like Net Zero.
The Role of AI in Reducing Operational Costs
AI as a Transformative Technology
The demanding need to effectively monetize 5G networks has placed CSPs in a position where operational cost reduction is paramount. Through the integration of AI into RAN operations, CSPs stand to achieve significant cost reductions and notable performance enhancements. AI is seen as a transformative technology that can address immediate financial goals while meeting long-term energy efficiency targets.
At the core of this transformative potential is AI’s ability to analyze vast amounts of data and make autonomous decisions that optimize network performance. By embedding AI into RAN operations, CSPs can ensure their networks run more efficiently and consume less energy. This is particularly important as RANs, according to GSMA Intelligence, account for a significant portion (87%) of an operator’s energy usage. Therefore, focusing on RAN optimization can substantially reduce overall energy consumption and costs.
Dynamic Shutdown of Radio Components
One of the early and most impactful applications of AI in this domain is the dynamic shutdown of radio components when they are not required to meet user demand. Traditionally, radio components remain active even during periods of low or no demand, leading to unnecessary energy consumption. AI changes this by continuously monitoring network traffic and dynamically shutting off radio components that are not needed.
This method leads to significant reductions in energy consumption without compromising the user experience. Operators can turn off parts of the network during off-peak hours or when traffic is minimal. Therefore, AI-driven dynamic shutdowns have become a critical strategy for CSPs aiming to enhance energy efficiency in their 5G networks.
Real-World Examples of AI-Driven Energy Savings
Umniah Jordan and Ericsson’s Intelligent RAN Power Saving Solution
Real-world examples illustrate AI’s effectiveness in achieving energy savings within 5G networks. One notable example is Umniah Jordan, which implemented Ericsson’s Intelligent RAN Power Saving solution. By leveraging AI technologies, Umniah Jordan reported a 20% daily power savings across its 5G network. This example underscores the substantial impact AI can have in enhancing energy efficiency.
Ericsson’s Intelligent RAN Power Saving solution comprises advanced AI algorithms that constantly analyze network conditions. These algorithms control the shutdown of certain RAN components when they are not needed, thus reducing overall energy consumption. The success of this deployment highlights the practical benefits of integrating AI into RAN operations, setting a precedent for other CSPs to follow.
Three UK and Ericsson AI-Powered Hardware
In a similar vein, Three UK employed Ericsson AI-powered hardware and software solutions, enhancing their energy efficiency efforts. Among these solutions, the dual-band Radio 4490 and various power-saving software features played pivotal roles in improving energy efficiency by up to 70% at select sites. This substantial energy efficiency improvement was achieved through AI’s ability to intelligently manage and allocate resources based on real-time traffic demands.
The AI-powered hardware solutions introduced by Ericsson for Three UK utilize machine learning algorithms to forecast traffic patterns and adjust energy consumption accordingly. This approach not only reduces energy usage but also ensures optimal network performance, demonstrating that AI can significantly impact energy efficiency and operational excellence in 5G networks.
Taiwan’s Far EasTone and Ericsson’s Service Continuity AI App Suite
Taiwan’s Far EasTone further exemplifies AI’s potential in enhancing energy efficiency. By trialing Ericsson’s Service Continuity AI App suite, Far EasTone managed to achieve a remarkable 25% daily RAN energy savings. This trial showcased AI’s ability to reduce power consumption while maintaining seamless service continuity, thereby supporting long-term sustainability goals.
The AI application suite deployed in Far EasTone’s network uses advanced machine learning techniques to monitor and manage network operations. By making data-driven decisions, the AI system can predict low-traffic periods and power down unnecessary components, significantly decreasing energy usage without impacting the quality of service. These real-world examples reflect the growing trend of CSPs adopting AI solutions to successfully reduce energy consumption and contribute to broader sustainability targets.
Balancing AI Energy Consumption and Sustainability
Differentiating AI Applications in RAN
While the benefits of AI in reducing energy consumption in RANs are evident, it is essential to differentiate between the energy consumption associated with various AI applications. Generative AI solutions often require power-hungry GPU clusters, leading to increased energy consumption. In contrast, AI and machine learning (ML) applications in RANs are typically more efficient, often offsetting their energy requirements by substantially lowering overall network energy usage.
In essence, the energy savings realized through AI-driven optimizations in RANs often outweigh the additional energy needed for AI processing. This efficiency comes from deploying AI in a manner that maximizes performance while minimizing energy consumption, thus creating a net positive impact on energy efficiency.
Integrating Sustainability into AI Development
Despite the advantages, it is crucial to acknowledge the growing energy consumption associated with AI technologies. The World Economic Forum (WEF) has emphasized the need to integrate sustainability into AI development processes to prevent exacerbating energy consumption issues. Beena Ammanath of WEF highlights that incorporating sustainability into AI is essential to align technological innovation with environmental responsibility.
AI development should consider the environmental implications alongside technological advancements. Sustainability integration in AI means designing algorithms and hardware that consume less power and optimizing systems to run more efficiently. By adopting a sustainable approach, the technology can continue to evolve while minimizing its environmental footprint and supporting the broader goal of achieving Net Zero.
Collaborative Efforts for Networked Sustainability
The AI RAN Alliance
Collaboration among industry leaders is crucial for achieving networked sustainability. One notable example is the AI RAN Alliance, founded by Ericsson and comprising companies like Microsoft, NVIDIA, SoftBank, and T-Mobile. This alliance aims to enhance network intelligence, efficiency, and reliability through collaborative efforts. Such partnerships are pivotal in pushing the boundaries of what AI can achieve in terms of energy efficiency and system performance.
The AI RAN Alliance focuses on making networks more self-sufficient and environmentally responsible. By pooling resources and expertise, alliance members are developing AI-driven solutions that not only boost network performance but also significantly reduce energy usage. This collaborative approach is instrumental in driving forward sustainability initiatives within the telecommunications sector.
Ayodele Damola’s Four-Step AI Journey
Ayodele Damola, Director of AI and ML Strategy at Ericsson, outlines a four-step AI journey aimed at leveraging AI to deliver advanced capabilities to customers. The journey comprises rules-based automation, autonomous features, cognitive intent-driven networking, and zero-touch networking. According to Damola, this journey is currently between autonomous features and cognitive intent-driven networking.
In this phase, operators can communicate high-level ambitions, such as maintaining certain service levels with minimal energy consumption, and AI functions within the network autonomously work to meet these requirements. This progression reflects the advanced state of AI deployment in CSP networks and the potential for further enhancements in energy efficiency and operational effectiveness.
AI-Driven Energy Efficiency in Practice
Vodafone’s AI-Powered Sleep Modes
One practical application that underscores AI’s potential in energy efficiency is Vodafone’s use of AI-powered sleep modes across a MIMO cluster. AI algorithms are utilized to forecast traffic patterns and make informed decisions about turning down specific components, such as antenna branches and power amplifiers, without manual intervention. This approach resulted in a 14% energy saving per site, illustrating the tangible benefits of AI in real-world network operations.
The AI-powered sleep modes operate by continuously learning from traffic data and optimizing network performance accordingly. This continuous learning capability ensures that the AI system adapts to changing conditions and regions, achieving consistent energy savings. By implementing AI-driven energy management solutions, Vodafone demonstrates how AI can enhance energy efficiency while maintaining robust network performance.
Continuous Learning and Improvement
The dynamic nature of AI means that systems not only optimize current operations but also continuously learn and improve over time. Through iterative learning processes, AI algorithms better understand traffic patterns and environmental conditions, enabling more precise and effective energy management. This ongoing improvement highlights AI’s potential for long-term enhancements in energy efficiency and network performance.
Ultimately, the constant evolution of AI tools and techniques ensures that energy efficiency efforts are always advancing. This perpetual improvement cycle positions CSPs to continually reduce energy consumption while delivering high-quality services. By harnessing the full potential of AI, CSPs can achieve a significant reduction in their environmental footprint, aligning with global sustainability goals.
Aligning Organizational Goals with Energy Efficiency
The Importance of Organizational Alignment
Aligning organizational goals with energy efficiency initiatives is essential for maximizing the benefits of AI. Demonstrating that AI can increase energy efficiency without degrading user experience can gradually build trust in AI technologies. This paradigm shift is crucial for widespread AI adoption in CSP networks and achieving significant energy savings.
Organizations must prioritize energy efficiency as a core objective and integrate it into their strategic plans. By doing so, they create an environment where AI-driven solutions are supported and encouraged. This alignment between organizational goals and energy efficiency drives innovation and reinforces the importance of sustainability.
Incentivizing Employees Based on Energy Efficiency
As communications service providers (CSPs) manage the intricacies of mid-5G deployment, artificial intelligence (AI) is proving to be a crucial technology for enhancing energy efficiency in 5G Radio Access Networks (RAN). CSPs are increasingly pressured to reduce operational costs while also meeting sustainability targets. The capacity of AI to optimize RAN functions addresses both immediate cost-saving goals and long-term sustainability aims. By adopting AI strategically, CSPs can boost their current network performance and simultaneously invest in advanced technologies for the future, thereby supporting broader initiatives such as Net Zero. This dual approach of leveraging AI not only helps in lowering operational expenses but also ensures that CSPs are making strides towards their environmental commitments, ultimately creating a balance between technological advancement and ecological responsibility. Using AI in RAN optimization is not just about cost-efficiency; it’s about laying the groundwork for a sustainable, future-ready network.