Locations
As we look to the future, the fusion of 6G and artificial intelligence (AI) is set to revolutionize connectivity and technological advancements. This leap from old-school, hardware-centric, manually driven networks to intelligent, sensing, self-training, and self-learning systems is made possible by 6G's unmatched capabilities and AI's steering power. But can AI truly succeed without the foundation of 6G? Let’s explore the potential, hurdles, and possibilities that await at the crossroads of 6G and AI.
Trends in 6G Evolution
6G technology is still in its initial stages, but it's already shaping up with enhanced capacity, ultra-low latency, ultra-high reliability, and improved privacy. These improvements will significantly enhance the performance of AI applications by facilitating faster data processing and real-time decision-making. Edge computing is another trend to watch, as it brings computational tasks closer to data sources, improving latency, privacy, and resource distribution across the network. The relevance of 6G to AI applications lies in its distributed computing network topology, cross-domain data fusion, and integrated sensing and communication. By converging AI with mobile and edge networks, 6G enables flexible AI model deployment and crowdsourcing of data from massive mobile and radio environments.
New AI-Based Applications Unlocked by 6G
Even in the pre-6G standard phase, we are already seeing glimpses of the potential of 6G networks. With advanced 5G, 6G will unlock new frontiers for AI-based applications in the following key areas:
- Data Efficiency: AI relies on efficient and low-latency data delivery to fuel its models. 6G has the potential to build a connectivity layer that efficiently feeds data to AI models, creating an ecosystem for diverse applications to flourish, including AR, VR, extended reality, remote patient care, smart cities, and smart grids.
- Proliferation of AI at the Edge: 6G is expected to enable AI to extend beyond mega-scale data centers to the edges, leading to the emergence of AI-powered personal computers and devices. This expansion will pave the way for a myriad of new applications, transforming user experiences and enabling futuristic technologies such as holographic communications and telepresence.
- Operator Opportunities: 6G presents an incredible opportunity for operators to integrate AI capabilities into their far-edge networks, empowering them to participate in new sets of AI applications as infrastructure providers. This convergence of AI and network infrastructure will not only revolutionize consumer applications but also enhance operational and management functions across various industries.
- Data Privacy and Sovereignty: With the rise of AI, data privacy and sovereignty have become paramount concerns. This creates opportunities for service providers to establish sovereign AI factories and leverage edge computing to address data privacy and security issues, thereby shaping the future landscape of AI applications.
The synergy between 6G and AI is set to unleash a wave of transformative applications, revolutionizing user experiences, empowering operators, and addressing critical data privacy and security challenges.
Critical Challenges for 6G to Support AI
We are still in the very early stages of 6G research, numerous challenges need to be resolved to effectively support AI based networks and applications. Some of these challenges include:
- Mobile Computing Network: The distributed topology of cloud and computing-based stations and mobiles presents a significant challenge for 6G networks.
- Data Fusion and Sensing: Integrating the sensing and communication base station to support sub-meter positioning of flying drones within a 1 km coverage area is a complex challenge that needs to be addressed.
- Efficacy and Reliability: Ensuring high efficacy and reliable interactivity for numerous AI robots is a critical challenge. Improving user data rates, device density, reliability, and latency compared to 5G is essential for meeting the demands.
- AI-Native Network and Interface: Creating a network that can support AI to work interconnectedly and collaboratively across the network is crucial. This involves reducing network complexity and cost by enabling a single AI algorithm or model to control multiple functions and end-to-end operations of the network.
In addition to these technical challenges, data governance and security are significant concerns for AI's efficient operation within the network. Establishing a robust data governance framework is essential for sharing data across the network and enabling data capabilities while ensuring privacy, security, and compliance with regulations.
The Sustainability Imperative
AI's energy-intensive nature, combined with the growing demand for AI-based applications, raises concerns about the potential increase in training costs during the transition to 6G. The urgency of addressing climate change amplifies the need to enhance 6G's energy efficiency. Fortunately, we can begin improving energy efficiency in radio applications without waiting for 6G. Open RAN architecture offers an opportunity to apply AI and ML on radio access networks, paving the way towards sustainable practices and zero traffic.
To address the power-intensive nature of training AI models, advancements in silicon and cooling technologies are necessary, including the adoption of direct liquid cooling in mega data centers to significantly improve energy efficiency. Encouraging the use of open-source models and implementing transfer learning can reduce the need for creating and training numerous individual models, promoting energy-efficient practices.
Streamlining model training and promoting adherence to data schemas and format standards are essential for reducing duplication and redundancy, ultimately contributing to more efficient energy usage. With 6G and 5G setting higher standards for consolidated applications, there is an opportunity to move towards a more unified approach, minimizing the need for multiple models and driving sustainable energy practices.
Conclusion
AI is already revolutionizing industries and driving innovation in diverse sectors, even without 6G. However, to unlock unprecedented advancements in connectivity, we must be prepared to harness the full potential of AI and seamlessly integrate it with forthcoming 6G technology. While standards and specifications are still a work in progress, it is essential to collaborate, establish standards, and define strategies for the successful integration of AI and 6G. Embracing AI now is not just an opportunity but a necessity, representing the gateway to unlocking the full potential of 6G and beyond.
