Sustainable AI in Telecom: A 6G Perspective

The integration of Artificial Intelligence (AI) into the telecommunications sector has ushered in transformative capabilities, particularly as the industry progresses towards 6G. However, AI’s environmental impact cannot be ignored. The latest whitepaper from the Next G Alliance, 'Sustainable AI in Telecom: Promises and Challenges in 6G', explores the dual role AI plays in achieving sustainability goals. It highlights how AI can enhance energy efficiency while also posing significant challenges due to its own high computational and energy demands.

The Role of AI in Sustainability

AI has emerged as a crucial tool for optimising energy consumption, improving supply chain efficiency, and accelerating the adoption of renewable energy within telecommunications. AI-driven automation in Radio Access Networks (RAN), core networks, user equipment (UE), and data centres enables better resource allocation and operational efficiency. These capabilities fall under two key concepts explored in the whitepaper:

  • AI for Sustainability – AI applications that enhance energy efficiency, reduce emissions, and optimise resource use.
  • Sustainable AI – Strategies to minimise AI’s own environmental footprint, considering the entire lifecycle of AI systems from development to retirement.

Key Challenges in Sustainable AI for Telecom

While AI presents opportunities to improve network sustainability, it also introduces challenges that must be addressed:

  • Energy Consumption and Emissions: AI model training and deployment require substantial computational resources, increasing energy consumption and carbon emissions. Data centres, which power AI systems, account for approximately 1% of global electricity demand—a figure expected to rise without intervention. AI/ML algorithms can help by optimising server workloads and adjusting power usage dynamically.
  • Water Usage for Cooling: Many data centres rely on water-intensive cooling systems, which can strain local water resources, particularly in arid regions. AI can play a role in optimising cooling efficiency by using real-time sensor data to fine-tune water flow and temperature, reducing overall consumption.
  • E-Waste and Hardware Sustainability: Outdated hardware contributes significantly to electronic waste, posing environmental and health risks. AI-driven asset management can help extend the lifespan of hardware through predictive maintenance, refurbishment, and responsible recycling practices.

Recommendations for AI-Driven Sustainability

To mitigate AI’s environmental impact, the Next G Alliance whitepaper outlines several strategies:

  1. Optimising AI Workflows – AI models should be designed for efficiency, reducing unnecessary computations and prioritising low-energy architectures.
  2. Sustainable Manufacturing – Encouraging the use of low-carbon materials and energy-efficient manufacturing processes to reduce embodied emissions in AI hardware.
  3. Extending Hardware Lifespan – Implementing upgrades and maintenance strategies to prolong the usability of network equipment and reduce e-waste.
  4. Circular Economy Practices – Promoting refurbishment, reusing, and recycling of computing components to minimise material waste.
  5. Standardisation and Regulatory Efforts – Supporting global efforts such as ISO, ITU-T, and CEN/CENELEC to establish frameworks that assess and mitigate AI’s environmental footprint.

Moving Forward: AI and 6G Sustainability

The transition to 6G presents a unique opportunity to embed sustainability into network design from the outset. AI-powered intent-based automation, spectral efficiency improvements, and integration with emerging technologies such as quantum computing and non-terrestrial networks (NTNs) can all contribute to a greener telecom ecosystem. However, a careful balance is needed to ensure that AI’s energy demands do not undermine these sustainability efforts.

The Next G Alliance’s research underscores the urgent need to align AI development with Net Zero objectives. While AI has the potential to drive significant environmental benefits, its implementation must be thoughtfully managed to avoid exacerbating energy and resource challenges. The telecommunications industry must continue exploring sustainable AI strategies to ensure that 6G networks contribute positively to global climate goals.

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