• Ubiquitous Intelligence: Embedding AI everywhere, enabling distributed intelligence for AI model training, inference, and deployment directly within the network infrastructure, extending to the network edge.
• Autonomous Operations: AI handles complex tasks like network optimization, resource allocation, and automated maintenance (O&M) in real-time, reducing reliance on manual intervention.
• AI-as-a-Service (AIaaS): The network transforms into a unified platform providing both communication and AI capabilities, making AI accessible for various applications.
• Intelligent Processing: AI drives functions across the air interface, resource management, and control planes for highly efficient operations.
• Data-Driven Automation: Leverages big data and real-time analytics to predict issues, optimize performance, and automate complex decision-making.
• Seamless User Experience: Moves beyond touchscreens to AI-driven interactions, offering more natural and contextual computing.

• Autonomous Operations: AI will enable self-monitoring, self-optimization, and self-healing networks, drastically reducing the need for human intervention in operation and maintenance (O&M).
• Dynamic Resource Management: ML algorithms will analyze massive amounts of network data in real-time to predict traffic patterns and user demands, dynamically allocating bandwidth, power, and computing resources to ensure optimal performance and energy efficiency.
• AI-Native Air Interface: AI/ML models will replace traditional, manually engineered signal processing blocks in the physical layer (e.g., channel estimation, beam management) to adapt dynamically to complex and time-varying wireless environments, improving spectral efficiency.
• Enhanced Security: AI will be critical for real-time threat detection and automated incident response across the hyper-connected 6G ecosystem, identifying anomalies and mitigating security risks that are not well understood by current systems.
• Digital Twins: AI will power the creation and management of real-time digital twins (virtual replicas) of the physical network, allowing for sophisticated simulations and testing of network changes before real-world deployment. 

• Pervasive Edge AI: AI model training and inference will be distributed throughout the network, from the cloud to the edge (devices, base stations), reducing latency and enabling real-time, localized decision-making for applications like autonomous driving and industrial automation.
• Support for Advanced Use Cases: The massive data rates (up to 1 Tbps), ultra-low latency, and high reliability enabled by AI in 6G will facilitate new applications such as holographic communication, remote robotic surgery with haptic feedback, and collaborative robotics that were not feasible with 5G.
• Federated Learning: The network will support distributed machine learning techniques, such as federated learning, which allow AI models to be trained on local data across various devices without the need to centralize sensitive user data, thus ensuring data privacy and security.
• Integrated Sensing and Communication (ISAC): AI will process the rich environmental data gathered through 6G’s new sensing capabilities (e.g., precise positioning, motion detection, environmental monitoring), allowing the network to interact with and understand the physical world in a holistic manner for applications like smart city management or augmented reality. 

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AI‑native air interface and RAN:

IMT‑2030 explicitly expects a new AI‑native air interface that uses AI/ML models for core PHY/MAC functions such as channel estimation, symbol detection/decoding, beam management, interference handling, and CSI feedback. This enables adaptive waveforms and link control that react in real time to channel and traffic conditions, going beyond deterministic algorithms in 5G‑Advanced.​

At the RAN level, IMT‑2030 envisions “native‑AI enabled” architectures that are simpler but more intelligent, with data‑driven operation and distributed learning across gNBs, edge nodes, and devices. AI/ML will be applied end‑to‑end for resource allocation, mobility, energy optimization, and fault management, effectively turning the RAN into a self‑optimizing, self‑healing system.​

Integrated AI and communication services:

The framework defines “Artificial Intelligence and Communication” (often phrased as Integrated AI and Communication) as a specific usage scenario where the network provides AI compute, model hosting, and inference as a service. Example use cases include IMT‑2030‑assisted automated driving, cooperative medical robotics, digital twins, and offloading heavy computation from devices to edge/cloud via the 6G network.​

To support this, IMT‑2030 includes “applicable AI‑related capabilities” such as distributed data processing, distributed learning, AI model execution and inference, and AI‑aware scheduling as native capabilities of the system. Computing and data services (not just connectivity) are treated as integral IMT‑2030 components, especially at the edge for low‑latency, energy‑efficient AI workloads.​

System intelligence and new use cases:

AI is central to several new IMT‑2030 usage scenarios beyond classic eMBB/mMTC/URLLC, including Immersive Communication, Integrated Sensing and Communication, and Integrated AI and Communication. In integrated sensing, AI fuses multi‑dimensional radio sensing data (position, motion, environment, even human behavior) to provide contextual awareness for applications like smart cities, industrial control, and XR.​

Embedding intelligence across air interface, edge, and cloud is seen as necessary to manage 6G complexity and enable “Intelligence of Everything,” including real‑time digital twins and AIGC‑driven services. The vision is for the 6G/IMT‑2030 network to act as a distributed neural system that tightly couples communication, sensing, and computing.

IMT 2030 Goals:

• To create self-healing, self-optimizing networks that can adapt to diverse demands.
• To enable new AI-driven applications, from intelligent digital twins to advanced immersive experiences.
• To build a truly intelligent communication fabric that supports a hyper-connected, AI-enhanced world.

​Summary table: AI’s roles in IMT‑2030:

In summary, AI in IMT‑2030 is both an internal engine for network intelligence and an exported capability the network offers to verticals, making 6G effectively AI‑native end‑to‑end.

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