Telecom AI: Achieving Energy Efficiency in the 5G Era

Energy efficiency is essential in today’s telecommunications ecosystem as 5G generates a far larger energy footprint than 4G/LTE. This resource identifies key Artificial Intelligence (AI) applications in making telco networks more sustainable.

Energy efficiency is essential in today’s telecommunications ecosystem as 5G generates a far larger energy footprint than 4G/LTE. This resource identifies key Artificial Intelligence (AI) applications in making telco networks more sustainable.

Telecom AI Trends

As the telecommunications industry fully transitions to 5G networks, there will be a greater emphasis on minimizing energy usage within Radio Access Network (RAN) infrastructure—where roughly 80% of mobile network energy is consumed. Although 5G provides higher transmission rates and ultra-low latencies, it generates a 3X to 5X larger energy footprint than 4G due to more power-hungry telco equipment and network densification. To solve this sustainability issue, Communication Service Providers (CSPs) should turn to Artificial Intelligence (AI) and Machine Learning (ML).

AI is already a part of many telco business functions, such as network security, customer service automation, and self-diagnostics. With sustainability becoming a bigger focus for 5G service providers, ABI Research anticipates that AI will become further infused into telecom operations. AI supports the following use cases: intelligent power-saving features, data center/base station cooling, predictive maintenance, and Virtual Power Plants (VPPs).

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“Forming partnerships with AI solution providers can help telcos co-develop energy-efficiency solutions and integrate AI into operations quickly. Collaboration among the wider telco ecosystem will be key to driving adoption of AI in the industry and identifying new cases and further opportunities for energy reduction. Mobile World Congress (MWC) 2024 saw the launch of the AI-RAN Alliance, an initiative to support efforts to integrate AI into RAN infrastructure.” – Alex McQueen, Analyst at ABI Research

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Network Optimization

AI is essential for optimizing mobile networks and reducing energy use. By implementing dynamic algorithms with machine learning, telco networks can automatically adjust to 5G traffic changes in real time. AI and ML tools help choose the most energy-efficient telco equipment configurations and power sources, reducing power consumption while satisfying user demands.

Nokia’s MantaRay energy solution lowers the power usage of radio networks, aiding CSPs in achieving zero-emission goals. It features MantaRay SON and MantaRay Network Management (NM), which automate energy-saving configurations and ensure AI/ML optimizations do not affect throughput or other Key Performance Indicators (KPIs). MantaRay SON also optimizes energy savings in multi-vendor RAN environments.

Read More: Generative AI in Telecommunications: Potential and Cautions

Intelligent Network Shutdown

AI-driven dynamic network shutdowns present significant energy-saving opportunities for telecom providers. Unlike older equipment with static sleep modes, AI optimizes energy use by adjusting to real-time traffic, potentially reducing overall energy consumption by up to 30%. For instance, ZTE’s Active Antenna Unit (AAU) hibernation technology lowers power use to less than 5 watts during no-traffic periods, integrating seamlessly with existing deep sleep and shutdown technologies.

Data Center/Base Station Cooling

Data centers consume massive amounts of energy, primarily due to the heat generated by servers and IT equipment. This necessitates effective cooling systems to curb telco operators’ carbon footprint. Sustainable solutions like Iceotope’s Precision Liquid Cooling, which uses dielectric fluid to eliminate nearly all heat from server components, and Huawei’s iCooling@AI, an intelligent chilled water system, optimize energy use. Combined with AI-driven algorithms and Internet of Things (IoT) sensors, these systems continuously monitor and adjust cooling based on real-time data. As a result, data center energy consumption is reduced by up to 15%.

Predictive Maintenance

Predictive maintenance is vital for CSPs to boost efficiency, reduce downtime, enhance customer experience, and increase revenue. AI applied to 4G/5G networks and data centers helps preempt faults by analyzing historical data patterns. This proactive approach extends equipment life span, optimizes Return on Investment (ROI), and cuts unnecessary costs.

Ericsson’s Predictive Cell Energy Management predicts traffic and usage patterns to optimize network performance and energy savings. It uses anomaly detection and predictive algorithms to monitor energy use, suggesting maintenance actions like changing air conditioning filters or adjusting cooling temperatures based on site-specific energy consumption profiles.

Virtual Power Plants

Virtual Power Plants (VPPs) have emerged as essential solutions for stabilizing the electric grid and enhancing energy efficiency for telco operators. By utilizing backup batteries to store excess power, VPPs help balance energy supply and demand. They also facilitate the integration of renewable energy by aggregating power from sources like solar and wind, releasing it when production drops or demand spikes. AI and ML optimize energy storage and consumption by forecasting demand and monitoring energy prices, creating revenue opportunities by selling stored energy to other ecosystem players. Elisa’s Distributed Energy Storage solution, aimed at removing up to 20,000 tons of Carbon Dioxide (CO2) emissions annually across its Finnish network, is a standout example of a telco VPP.

Key Companies in Telecom AI

• Elisa
• Ericsson
• Fujitsu Limited
• Huawei
• Iceotope Technologies Limited
• Intel Corporation
• Inveniam
• Net AI
• Nokia
• Tupl
• ZTE Corporation

Learn More

To better understand how AI is used for telco energy efficiency and how ecosystem vendors are meeting the demand, download ABI Research’s The Role of AI in Building Sustainable and Energy-Efficient Telco Networks report.