As Europe rapidly expands renewable energy capacity, energy storage systems, and electrification initiatives, project developers are under increasing pressure to accelerate grid connections for new power generation assets and energy-intensive facilities while complying with more demanding standards for sustainability, reliability, and project execution.

One of the key technologies showcased by Daqo was the DQS Air Series SF6-free ring main unit (RMU). Utilizing dry-air insulation combined with vacuum interruption technology, the DQS Air system offers an environmentally responsible alternative to traditional SF6-based medium-voltage equipment. Its compact and fully sealed construction is designed to minimize maintenance requirements while ensuring dependable performance across urban power networks, renewable energy installations, industrial applications, and grid modernization projects.

Daqo also unveiled its newest SF6-free air-insulated RMU solution developed for secondary distribution networks. Engineered with flexible configuration options, intelligent monitoring capabilities, and remote operation readiness, the system is intended to help customers build more sustainable, efficient, and easier-to-manage power distribution infrastructure.

For renewable energy and battery storage applications, the company presented its containerized photovoltaic (PV) and battery energy storage system (BESS) substation solutions. These integrated systems combine step-up transformers, medium- and low-voltage switchgear, monitoring platforms, and auxiliary equipment within a factory-assembled unit. This approach enables developers and EPC contractors to reduce on-site complexity, streamline project coordination, and accelerate the timeline from installation to grid connection.

Addressing the requirements of power-intensive industries such as data centers, Daqo showcased DQMpower 2.0, a modular power platform that consolidates low-voltage distribution, UPS-related functionality, power quality management, and monitoring systems. The solution is designed to improve deployment speed, maximize the utilization of electrical room space, and provide greater operational transparency. The company's solid-state transformer technology also attracted attention as an advanced solution capable of supporting more flexible interaction between electrical grids, energy storage systems, loads, and DC power networks.

"Our discussions with customers at The smarter E Europe reinforced a clear industry priority: energy infrastructure projects require cleaner technologies, faster grid access, and more predictable project delivery," said Kevin Lin, General Manager for Daqo's European region. "Daqo continues to expand its European coordination capabilities and partner ecosystem to provide customers with integrated technical solutions, simplified project interfaces, and enhanced delivery support."

Daqo's strategy in Europe centers on combining large-scale manufacturing capabilities with localized coordination and partner collaboration. The company operates through a network of 32 manufacturing entities, providing expertise across medium- and low-voltage electrical equipment, transformers, automation technologies, and integrated power systems.

For European customers, this approach offers streamlined technical solutions, reduced supplier complexity, and improved control over project delivery. Moving forward, Daqo plans to deepen collaboration with utilities, developers, EPC contractors, and industrial clients to advance the deployment of cleaner, more reliable, and rapidly deployable power infrastructure throughout Europe.

Telecom providers are pushing for fast 6G standardization and quick adoption across enterprise and consumer markets, with AI playing a central role.

The transition to 6G marks a major shift — potentially becoming the first generation of wireless networks built to be AI-native. Because AI will deeply influence how 6G operates, engineers face a new challenge: validating systems that are far more adaptive, intelligent, and fast than previous generations.

This overview outlines how AI can support 6G design validation for teams in communication service providers, mobile operators, technology vendors, and device manufacturers. It also explores emerging applications enabled by AI and 6G, the types of AI techniques involved, and how these tools can streamline design and testing workflows.
 
What new opportunities will 6G and AI unlock?
AI and 6G together are expected to drive major innovations, including real-time digital twins, advanced manufacturing systems, highly autonomous transport, holographic communication, and widespread edge intelligence. These capabilities align with the visions of the ITU and 3GPP for 2030 and beyond.

Real-time digital twins
With widespread coverage, extremely low latency, and high throughput, 6G paired with AI could create high-fidelity, real-time digital counterparts of physical assets and environments. These digital twins would support modeling, control, analysis, and simulation with unprecedented accuracy. Digital twin networks could mirror actual network conditions to enable continuous optimization, especially when combined with integrated sensing and communication (ISAC).

Smart factories
AI-enhanced 6G connectivity could enable industrial automation at scale through reliable, ultra-responsive data exchange across robotics, industrial IoT, and intelligent devices. “Industrial 6G” may enable fully automated operations in environments such as factories, ports, and airports, supported by private 6G deployments.

Autonomous mobility
Next-generation mobility systems — from autonomous vehicles to intelligent transportation — will rely on AI-powered 6G capabilities. This includes AI-assisted driving, real-time mapping, and precise positioning for cellular vehicle-to-everything (C-V2X) interactions.

Holographic communication
Future 6G and AI infrastructure may support immersive communications such as holographic telepresence and multi-sensory remote interaction. AI-driven semantic communication could reduce bandwidth demands by transmitting only the essential meaning behind data-heavy content.

Distributed edge intelligence
6G is expected to blur the line between communication and computing by pushing AI models to the network edge. This could enable coordinated inference, collaborative robotics, and pervasive, real-time intelligence across devices.
 
How will AI improve 6G network design and operations?
6G will involve both physical elements (e.g., radios, base stations, user devices) and logical components (e.g., RAN, core network functions, protocol stacks). Many of these will be optimized using AI during design, validation, and even runtime.

AI-native air interface
AI could enhance key radio functions such as channel estimation, symbol detection, beam selection, modulation, and antenna configuration. These models may operate on devices, at the base station, or jointly across both.

AI-assisted beamforming
AI methods may support:

AI-optimized RAN
AI could enable a self-organizing RAN capable of real-time adaptation, end-to-end optimization, and autonomous performance tuning.

Automated network management
AI-driven operations may include predictive maintenance, traffic forecasting, energy optimization, and intelligent resource allocation. Real-time threat detection and mitigation could also be enhanced through AI analytics.
 
Which AI techniques are most useful for validating 6G performance?
A range of AI methods — deep learning, reinforcement learning, generative models, and more — will support system-level design and testing.

Reinforcement learning (RL)
RL is well-suited for automating decision-making in unpredictable environments and may be applied to:

Deep neural networks (DNNs)
DNNs may support tasks such as:

Transformer models
Transformer autoencoders may enhance CSI compression and feedback efficiency.

Graph neural networks (GNNs)
GNNs can model network topology and spatial relationships for interference control, mobility forecasting, and resource allocation.

Generative adversarial networks (GANs)
GANs can generate realistic channel data, support denoising, and detect anomalies.

Large reasoning/action models
These emerging agentic models may coordinate complex workflows and help test sophisticated, multi-component 6G systems.
 
How will synthetic AI data support 6G testing and validation?
AI-generated data will be crucial for exploring the huge range of possible 6G conditions — many of which cannot be physically tested early on.

Key synthetic-data methods include:

 
Can AI help validate 6G hardware and chip designs?
Yes. AI-powered anomaly detection, automation, and data-driven modeling could support the design of components for sub-THz frequencies, UM-MIMO, and other 6G features.
Key methods include:

 
What challenges come with using AI for 6G validation?
AI’s reliability isn’t guaranteed. Issues include out-of-distribution errors, limited data, low interpretability, overfitting, and hallucinations. To improve trustworthiness:

 
Keysight’s role
This summary illustrates how AI can support 6G design and testing. Keysight provides tools, research expertise, and 6G-ready test solutions to help engineering teams innovate with confidence throughout development.

Click here to know more.