Why Do We Need AI in 6G?

With 5G, AI was primarily a supporting tool used to optimize network performance. However, the complexity of 6G networks, which will operate at terahertz frequencies and support billions of connected devices, makes manual optimization nearly impossible. The increased scale and complexity introduce several key challenges that AI is uniquely positioned to solve.

One major challenge is real-time decision-making. 6G networks will need to process and respond to events faster than ever before, such as rerouting data for autonomous cars or managing virtual environments in the metaverse. Another challenge is dynamic resource allocation—efficiently managing bandwidth, power, and computing resources across billions of devices in diverse environments. Additionally, 6G networks will require self-healing capabilities, where the network can autonomously detect and fix faults to maintain ultra-reliable connections.

How 6G Solves This

6G is AI-native, meaning that AI and machine learning (ML) are not just tools but integral components of its architecture. AI transforms the 6G landscape in several key ways:

AI for Predictive Network Management: Traditional networks react to events like congestion or failures only after they happen. In contrast, AI in 6G predicts these issues before they occur, allowing the network to optimize routes and resources in real time, improving efficiency and reliability.

Edge AI for Ultra-Low Latency: Processing data in centralized clouds introduces delays, which is unacceptable for applications requiring real-time responses. With 6G, AI models will run directly on embedded devices at the edge, enabling faster decision-making and minimizing latency.

AI-Powered Signal Optimization: Managing interference and attenuation at terahertz frequencies is highly complex. AI will dynamically adjust signal paths and beamforming parameters to ensure strong and consistent connectivity, even in challenging environments.

Use Cases Driving AI-Enabled 6G

AI in 6G will unlock new possibilities across various industries:

Autonomous Systems: AI will enable ultra-reliable, low-latency communication for fleets of self-driving cars, drones, and delivery robots. For example, in smart agriculture, AI-equipped drones will analyze crop health in real time and coordinate with ground-based machines to optimize resource use and improve yield.

Digital Twins in Smart Manufacturing: AI will enable real-time synchronization of physical and virtual worlds, improving monitoring, simulation, and optimization. In a smart factory, for example, every machine’s digital twin can predict failures and autonomously adjust production schedules to maximize efficiency.

Personalized Healthcare: AI will process health data from wearable devices, providing real-time insights and alerts to improve patient care. Continuous glucose monitoring with predictive AI for diabetics is one such example, ensuring timely interventions based on real-time data.

Dynamic Metaverse Experiences: AI will adapt virtual environments in real time, ensuring smooth interactions and seamless transitions. For instance, a virtual concert can adjust in real time based on the preferences and locations of millions of users, creating a highly personalized experience.

Standards and Frameworks to Explore

Embedded engineers can refer to several key standards and frameworks to understand how AI integrates with 6G networks. The ETSI ENI (Experiential Networked Intelligence) framework focuses on AI-based network optimization. 3GPP Release 20 is dedicated to embedding AI into 6G architecture, while ITU-T Y.3172 outlines the architectural framework for AI in future networks. Additionally, OpenAI’s Edge AI Research provides practical models for implementing on-device intelligence, helping engineers adapt to the new AI-driven era.

Why Embedded Engineers Must Focus on AI

In the 6G era, every embedded system will become a smart system. Embedded engineers with expertise in on-device AI, edge intelligence, and real-time processing will play a crucial role in shaping the future of intelligent connectivity. Understanding how to implement AI at the edge and optimize network performance using predictive models will be essential for driving the evolution of 6G.

Stay tuned for the next post, where we’ll discuss Energy Efficiency and Power Management and another critical challenge in 6G networks.