The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising unprecedented levels of openness, flexibility, and intelligence within radio access networks. Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms.

iTwins-Lab works on different aspects of O-RAN which include

• AI-based sustainability and energy efficiency, 

• AI-based service guarantee and network slicing,

• Experimentation and test

• Software (xApp/rApp) development  

In wireless and mobile networks, many decisions must be made frequently and in real-time. These decisions include everything from power and spectrum allocation to packet routing and device positioning. The best decision-making technique depends on the network's structure, whether it's centralized, hierarchical, or a complex mix of competitive and cooperative elements.

At iTwins, we specialize in applying a wide range of decision-making techniques to solve these complex problems. Our expertise includes using optimization, game theory, auction theory, meta-heuristics, and machine learning to enhance wireless networks and the Internet of Things (IoT).

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The transition to 6G is expected to bring significant advancements, including much higher data rates, enhanced reliability and ultra-low latency compared to previous generations. Although 6G is anticipated to be 100 times more energyefficient, this increased efficiency does not necessarily mean reduced energy consumption or enhanced sustainability. Network sustainability encompasses a broader scope, integrating business viability, environmental sustainability, and social responsibility. Our research explores the sustainability requirements for 6G and proposes Open RAN as a key architectural solution. By enabling network diversification, fostering open and continuous innovation, and integrating AI/ML, Open RAN can promote sustainability in 6G. We identified high energy consumption and e-waste generation as critical sustainability challenges and discusses how Open RAN can address these issues through softwarisation, edge computing, and AI integration.