DeepSig's AI-Native Tech Aims to Redefine 5G and 6G Networks

By Brian Richardson

BARCELONA, Spain – February 27, 2026 – As the global technology community converges on Mobile World Congress 2026, Arlington-based DeepSig is poised to demonstrate a fundamental shift in how wireless networks are built and operated. Through strategic alliances with industry giants like NVIDIA and PCTEL, the company is moving the concept of "AI-native" wireless from academic theory to real-world application, showcasing solutions that promise to enhance everything from next-generation 6G networks to mission-critical tactical communications.

At the heart of DeepSig’s showcase is a paradigm shift away from traditional, rigid wireless systems. Instead of merely applying artificial intelligence as an optimization layer, the company is embedding deep learning directly into the core functions of the network. These demonstrations signal a growing ecosystem embracing AI not as a feature, but as the foundational operating principle for future wireless infrastructure.

The AI-Native Paradigm Shift

For years, the term AI has been a buzzword in the telecom industry, often referring to algorithms that optimize existing, predefined processes. DeepSig's "AI-native" approach represents a more radical departure. It involves rebuilding core components of the wireless physical layer (PHY) using AI and machine learning, allowing the network itself to learn and adapt to its environment in real time.

Unlike traditional systems based on fixed mathematical models, an AI-native system is designed for continuous, closed-loop learning. This enables unprecedented adaptability, allowing the network to dynamically manage bandwidth, power, and spectrum to maximize efficiency and minimize congestion. DeepSig's OmniPHY-5G software, for example, replaces traditional signal processing blocks with neural networks, a change that can yield significant throughput gains and improve power efficiency. It's a move from a system that is engineered to one that learns.

This approach promises tangible benefits. By optimizing the physical layer with data-driven AI methodologies, networks can achieve better coverage, higher capacity, and increased reliability. The system becomes more resilient, capable of self-healing and self-organizing to maintain performance even in the complex and crowded spectrum environments that define modern communications.

Forging the Path to 6G with Strategic Alliances

The viability of this next-generation technology is being proven through key collaborations. At MWC, DeepSig, alongside NVIDIA, SRS, and the AI-RAN Alliance, is demonstrating a pre-6G fully learned waveform. This advanced system runs on a 3GPP Release 17 GPU-accelerated software stack for base stations (OCUDU) powered by the compact NVIDIA DGX Spark platform.

The demonstration is a significant milestone, illustrating a scalable, carrier-grade base station that can support both current 5G and emerging 6G devices. NVIDIA’s role is critical, providing the high-performance GPU infrastructure necessary to process the complex AI models in real time. This integration of GPU-accelerated RAN with an AI-native air interface is a crucial step on the roadmap to 6G, where such computational power will be essential.

The involvement of the AI-RAN Alliance, of which DeepSig is a founding member, underscores a broad industry consensus. The alliance aims to integrate AI into cellular technology to advance Radio Access Network (RAN) capabilities, improve spectral efficiency, and reduce power consumption. The pre-6G learned waveform demo is a direct result of this collaborative vision, showing a clear path toward intelligent radio access networks where the air interface itself is optimized by AI.

Enhancing Intelligence at the Tactical Edge

Beyond commercial mobile networks, DeepSig is demonstrating the profound impact of AI-native technology on critical communications. A separate partnership with PCTEL integrates DeepSig's OmniSIG spectrum awareness software into PCTEL’s new SeeHawk® Scout, a tactical signal intelligence platform.

This collaboration transforms the portable receiver into a powerful intelligence tool. OmniSIG uses deep learning to detect and classify signals across cellular and non-cellular bands in real time—a process it can perform up to 100 times faster than traditional methods. The integrated platform can identify sources of interference, locate rogue or unauthorized base stations, and provide actionable intelligence to users in the field.

The applications are vast, spanning defense, public safety, and regulatory enforcement. For military and intelligence users, it offers superior battlefield awareness. For first responders, it helps ensure critical communication links are free from interference during emergencies. For network operators and regulators, it provides a powerful tool for managing and securing an increasingly congested radio spectrum.

“Our focus is on embedding intelligence directly into wireless systems,” said Jim Shea, CEO of DeepSig, in a statement. “Whether operating inside open CU/DU architectures or within portable spectrum intelligence platforms, AI-native processing enables faster signal understanding, improved interference detection, and more adaptive network performance.”

Navigating Market Adoption and Industry Hurdles

Despite the clear technological promise, the path to widespread adoption of AI-native wireless is not without challenges. The primary hurdles are economic and practical. Deploying AI in RAN infrastructure requires substantial investment in high-performance hardware like GPUs, which can increase both cost and power consumption—a significant concern for operators in a competitive market.

Furthermore, integrating and managing the complexity of AI models across diverse, multi-vendor Open RAN environments presents significant technical and operational challenges. The industry also faces a skills gap, requiring a new generation of engineers who are proficient in both telecommunications and machine learning.

Regulatory frameworks are also struggling to keep pace. As AI becomes embedded in critical national infrastructure, questions of governance, security, transparency, and bias come to the forefront. The industry, through bodies like the AI-RAN Alliance, is working to establish ethical principles, but a clear legal and regulatory landscape has yet to fully form.

DeepSig’s MWC demonstrations represent a crucial step in proving the value proposition of AI-native systems. By showcasing tangible performance gains and enabling new capabilities in both commercial and mission-critical sectors, the company and its partners are building the case that the significant upfront investment and operational shifts are necessary to unlock the next era of wireless innovation.