AI Centric Network Concept

Overview

"Network for AI" focuses on building a network foundation that connects AI to mobile networks and maximizes its performance potential.
Conventional mobile networks, particularly 4G and 5G, have primarily delivered a wide range of services by connecting human-centric devices, such as smartphones and IoT devices.

Looking ahead to the 2030s, services centered on non-human entities—such as AI systems and robots—are expected to be deployed across a wide range of scenarios. In this context, the role of the network will become increasingly important—not merely as a means of connectivity, but as a platform that enables continuously evolving AI and robots to fully realize their capabilities.

For example, large-scale and efficient AI training and inference require the ability to process massive volumes of data and provide feedback in real time.
Achieving this demands data collection and computational processing at a scale that exceeds the limits of conventional network, requiring further improvements in throughput, capacity, latency, reliability, energy efficiency, and sustainability.

Against this backdrop, 6G networks are emerging as an indispensable next-generation foundation for the evolution of AI and robotics.
6G is expected to go beyond its role as a mere communication infrastructure and serve as a fundamental platform that enables AI and robots to operate across a wide range of societal applications.
Data speed and volume, combined with intelligent processing capabilities, will play a critical role in driving the advancement of next-generation technologies.
To fully unlock the potential of these emerging technologies, we are pursuing innovative approaches and tackling technical challenges toward 6G, with the aim of building a sustainable society.

Through the power of AI and robotics, we seek to develop 6G networks that enable a more convenient and efficient society.

Network for AI

Create value

Around 2030, it is envisioned that people will use AI seamlessly in their daily lives without consciously being aware of it, while robots will play active roles across a wide range of everyday situations.

By supporting large-scale data collection and computational resources within the network, 6G networks will enable a harmonized world in which humans, AI, robots, and machines coexist and collaborate—an ecosystem of Harmonized Intelligence.

Overview

"AI for Network" refers to initiatives that apply AI technologies directly to communication control and operational maintenance within mobile networks, enabling more efficient and advanced network operations.

These initiatives did not begin with 6G; significant progress has already been achieved in existing 4G and 5G networks.
Concrete examples include the allocation of frequency bands in areas where communication traffic is highly concentrated, automated optimization functions, and the automation of network operations.

Through these approaches, smoother and more stable communication environments have been realized.

However, several challenges have emerged with conventional methods.
As network equipment becomes increasingly multifunctional and complex, challenges have emerged, such as longer recovery times and broader impact in the event of large-scale disasters or unforeseen incidents.

In such situations, the ability to respond swiftly becomes a critical challenge.
Within "AI for Network," efforts to address these challenges are being accelerated.

"In particular, these initiatives focus on eliminating human error, pursuing networks with minimal service disruption—so-called Zero Outage networks—and maximizing customer experience through the use of advanced technologies.
By applying AI to these areas, communication control and network operations and maintenance are expected to become more reliable, flexible, and efficient."

AI for Network

Create value

As the range of services provided over mobile networks continues to expand, there is a growing need to improve efficiency by addressing increasing network complexity, as well as the time and cost required for operations.

At the same time, AI technologies are evolving at a remarkable pace. By applying AI to networks, it becomes possible to capture changes in network conditions efficiently and rapidly, and to enable automated optimization of network operations.

One example of initiatives to improve transmission efficiency at the radio interface through the use of AI is the AI-native Air Interface (AI-AI).

This approach designs modulation schemes tailored to radio propagation environments and applies them to the air interface to enhance communication performance.
In addition, by leveraging AI, signaling for radio propagation channel estimation between transmitters and receivers in communication equipment and base stations can be reduced or eliminated, enabling significantly higher transmission efficiency.