Net AI: Pioneering Smarter, More Efficient Telecom Networks 🌐

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As the demand for high-speed connectivity continues to grow, mobile operators are struggling to keep up. Their current approach—expanding Radio Access Networks (RANs)—is not sustainable. RANs account for 80% of all mobile network energy consumption, with energy costs outpacing sales growth by over 50%.

Recognizing this growing challenge, Net AI was founded to develop AI-driven solutions that address network efficiency and sustainability. The mission is to bridge the gap between telecommunications and machine learning, enabling operators to optimize the performance of their networks while reducing energy consumption.

Industry Status Quo

Most actors in the telecommunications industry believe that AI systems are not accurate enough in their predictions of traffic demands and would consume too much energy themselves. This stems from a disconnect between the telecom and machine learning (ML) communities.

Traditionally, telecom networks have relied on rigid, rule-based optimization techniques designed for deterministic systems, while ML involves building statistical models based on extensive data used for training. Because of this fundamental difference, many telecom professionals are skeptical about allowing AI to make decisions that could impact network reliability. On the other hand, ML experts often underestimate the strict performance requirements and real-time constraints specific to telecom networks. This disconnect has prevented the industry from fully realizing AI’s potential in optimizing network operations. Bridging this gap requires specialized AI models tailored to telecom environments, ensuring both accuracy and efficiency without compromising service quality.

Meet Net AI

Net AI has been developing domain-specific ML/AI models that can help telecom operators reduce energy consumption and improve network efficiency. Our research has shown that significant energy savings—up to 60%—can be achieved by selectively deactivating parts of the network infrastructure during low-demand periods. However, this must be done carefully to ensure that service quality is not compromised.

Its approach focuses on:

📡 Accurate Traffic Forecasting: Providing real-time insights into network performance and resource usage to enable informed decision-making.

⚡ Efficiency Improvements: Helping operators optimize network resources to manage energy costs while maintaining service quality.

🔎 Proactive Network Management: Identifying unusual patterns in traffic to predict and prevent network incidents.

Our team’s unique expertise at the intersection of AI and telecoms has enabled us to build perhaps the most accurateAI-driven forecasting engine on the market (patent pending). What makes it different?

🚀 Scalability: Our AI is 100 times more scalable than competitors and inherently low in power consumption. We have achieved this via domain-specific neural architecture design.

🎯 Performance: We achieve over 95% accuracy in traffic forecasting. Our data handling pipeline employs pre-processing that improves the sensitivity and generalization ability of trained models.

🔒 Reliability: Our proprietary training methodology ensures that AI models avoid underestimation errors, crucial for maintaining service quality.

By challenging the widely held perception that service disruption risks and computational cost outweigh the benefits of AI-driven forecasting, we’ve created a solution that gives operators confidence to automatically and intelligently switch off targeted network infrastructure while maintaining service quality.

With additional integration work, similar strategies could be applied in data centers, addressing energy efficiency challenges on a broader scale.

Shaping the Future of Telecoms 🌍📶

The telecommunications industry is at a critical juncture where intelligent automation and energy efficiency are becoming key priorities. As AI continues to evolve, its role in infrastructure management will expand, providing new opportunities to enhance service quality while reducing operational costs.

Net AI is playing an active role in this transformation. By combining expertise in AI and telecom, they aim to contribute to a more efficient, intelligent, and sustainable future for network operations. Their work within the ETHER project further supports this goal as it focuses on improving network coverage and energy efficiency through integrated terrestrial and non-terrestrial networks.

Net AI has already demonstrated that AI-driven solutions can play a meaningful role in optimizing telecom infrastructure. As the industry moves forward, we continue to explore new ways to refine and expand these capabilities.