The evolution of wireless networks is entering a transformative phase with AI-native Radio Access Networks (RAN), enabling real-time learning, adaptability, and spectrum optimization. AI-powered RAN replaces rigid, hardware-defined systems with adaptive, software-driven architectures, laying the foundation for 6G networks. DeepSig, a pioneer in AI-native wireless communications, is at the forefront of this shift, advancing AI-powered air interfaces and intelligent spectrum management. At NVIDIA GTC 2025, DeepSig CEO and Co-Founder Jim Shea will join industry and academic leaders to explore AI-native RAN’s role in shaping 6G, highlighting key breakthroughs in network efficiency, adaptability, and performance.
The Rise of AI-Native RAN
Traditional RAN systems rely on predefined hardware architectures, limiting adaptability and requiring manual optimization. AI-native RAN introduces:
- Real-Time Learning: AI-driven models continuously optimize network performance based on real-time data.
- Intelligent Spectrum Utilization: Advanced AI algorithms dynamically allocate spectrum resources to reduce interference and maximize efficiency.
- AI-Optimized Air Interfaces: Machine learning replaces traditional pilot signals and modulation to improve throughput and power efficiency.
DeepSig’s Innovations in AI-Native RAN
1. OmniPHY®: AI-Driven Air Interface
At Mobile World Congress (MWC) Barcelona 2025, DeepSig demonstrated OmniPHY®, its AI-native air interface, running on the NVIDIA AI Aerial platform. Key advancements include:
- Up to 70% Throughput Gains: AI-optimized waveform processing enhances network efficiency.
- Elimination of Traditional Pilot Overhead: AI replaces redundant signals, improving spectrum utilization.
- Enhanced Power Efficiency: Neural network-driven air interfaces reduce energy consumption.
2. AI-Native RAN Simulation with NVIDIA Aerial Omniverse Digital Twin
DeepSig leverages digital twins to train and optimize AI-native RAN models, simulating real-world deployment scenarios. Benefits include:
- Precise Network Optimization: AI models are trained on site-specific conditions for optimal real-world performance.
- Rapid Testing & Deployment: AI-driven simulations accelerate RAN model improvements before live deployment.
- Improved Network Adaptability: Digital twins allow continuous refinement of AI models to enhance network resilience.
3. Defining AI-Native RAN for 6G at NVIDIA GTC 2025
At NVIDIA GTC 2025, Jim Shea will join experts from NVIDIA, MIT WINSLab, Samsung Electronics, and other industry leaders to discuss:
- How AI is redefining wireless network architectures
- The role of AI in improving RAN performance and scalability
- How AI-native RAN will accelerate the transition to 6G
4. The Future of AI-Native Wireless Networks
DeepSig envisions AI-native RAN as the foundation for next-generation networks, enabling:
- Fully automated, self-optimizing wireless systems
- Faster, more energy-efficient connectivity
- Seamless AI-driven 6G infrastructure
AI-native RAN is not just an upgrade—it’s a paradigm shift in how wireless networks are designed and operated. By leveraging real-time AI learning, intelligent spectrum allocation, and adaptive air interfaces, DeepSig is driving the next era of wireless innovation.
With OmniPHY® and AI-native RAN solutions, DeepSig is paving the way for faster, more efficient, and AI-powered 6G networks. As the industry moves toward a software-driven, intelligent future, DeepSig remains at the forefront, transforming wireless communications.
