The Future of Wireless Systems: Embracing AI in 5G Advanced and 6G

What You Need to Know

• On-device AI empowers E2E optimization by harnessing the computational power of millions of devices and preserving data privacy.
• 5G Advanced’s Release 18 introduces AI-driven use cases like dynamic channel feedback and intelligent beam management for enhanced performance.
• The AI-native 6G air interface will usher in a data-driven approach, enabling continuous performance improvements and customizable wireless networks for diverse applications.

The ever-increasing complexity of wireless systems and the transition to 5G have brought about a surge of interest in generative AI. This article explores the critical role that AI will play in future wireless systems, specifically focusing on the advancements in 5G Advanced and the potential for AI-native 6G. As we delve into this topic, we will also touch upon the importance of end-to-end (E2E) systems optimization and the challenges associated with it.

Before we begin, let’s briefly introduce the Smart Transfer app, a third-party application that can streamline the optimization of your contacts, ensuring a smooth and seamless experience when you connect with others. The Smart Transfer app uses advanced algorithms to identify and remove duplicate contacts, clean up outdated information, and merge duplicates. With its user-friendly interface, this contacts optimizer app simplifies the process of organizing and managing your contacts, saving you time and effort.

Optimizing Wireless Systems: The Role of AI

As 5G Advanced comes into the spotlight, wireless AI is set to become a fundamental pillar of its design and operation. The innovative AI applications integrated into 5G Advanced networks promise to enhance the performance and efficiency of both networks and devices over the next few years. Furthermore, the advent of 6G will bring about a paradigm shift in wireless AI, making it native and all-pervasive across devices, networks, and protocols.

E2E Systems Optimization: The Power of On-Device AI

AI has already made its mark on smartphones and other devices, but its implementation has mostly been confined to individual devices or networks. This limitation has hindered the realization of true E2E systems optimization across multiple devices and networks. However, recent advancements in on-device AI have opened up new possibilities. On-device AI enables processing to be distributed across millions of devices, leveraging their collective computational power. This approach allows for customized AI model learning based on each user’s personalized data, without compromising data privacy. As on-device AI extends beyond smartphones to various devices, such as sensors and industrial equipment, its impact on E2E optimization will only grow.

Cross-Node AI for E2E Performance Optimization

While on-device AI improves E2E performance on a device level, the full potential of systems optimization is achieved when AI training and inference are done collaboratively across both the network and devices. This cross-node AI is a key focus in 5G Advanced, as it opens the door to various use cases defined in 3GPP’s Release 18 specification. These use cases set the groundwork for the widespread use of wireless AI in 6G.

Wireless AI in 5G Advanced: Release 18 Use Cases

3GPP’s Release 18 sets the stage for a more extensive application of wireless AI in 6G. It prioritizes three use cases that demonstrate the potential of AI-driven improvements in wireless systems:

• Dynamic Channel State Information (CSI) Feedback: By using cross-node machine learning, the CSI feedback mechanism between base stations and devices can be dynamically adapted, leading to coordinated performance optimization between networks and devices.
• Intelligent Beam Management: Machine learning is employed to intelligently manage beams at both the base station and the device. This optimized beam management improves network capacity and device battery life.
• Enhanced Positioning Accuracy: ML is used to enhance positioning accuracy for devices in indoor and outdoor environments. This includes both direct and ML-assisted positioning, enhancing location-based services.

AI-Native 6G Air Interface: A Disruptive Leap Forward

To meet the demands of faster data rates, increased capacity, and new use cases in 6G, the air interface must undergo a significant leap in performance and efficiency compared to 5G. This is where AI-native design comes into play. An AI-native 6G air interface is characterized by a data-driven approach, integrating machine learning across all protocols and layers with distributed learning and inference implemented across devices and networks. This revolutionary shift will enable continuous performance improvements, adaptability to specific environments, and dynamic customization of wireless networks for various applications.

Final Thoughts

As wireless systems embrace AI, 5G Advanced and the upcoming 6G will shape the future of communication networks. From on-device AI enhancing E2E optimization to cross-node AI facilitating collaborative systems optimization, the potential of wireless AI is vast. As we prepare for the arrival of AI-native 6G, the wireless industry is on the cusp of a transformative era where machine learning and AI will empower networks and devices like never before. And don’t forget to optimize your contacts with the Smart Transfer app, ensuring seamless communication in this AI-driven world.