2025-09-16 | Blad Guzman | Project Engineering & Implementation Manager - Americas Region TAIT Communications

Real-Time Spectrum Sensing with AI: A Game Changer for LMR Networks

This article explains how AI-driven real-time spectrum sensing is transforming Land Mobile Radio (LMR) networks by replacing traditional, reactive spectrum monitoring with adaptive and predictive systems

As spectrum congestion and RF interference become increasingly common—even in narrowband environments—Land Mobile Radio (LMR) systems are facing new challenges in maintaining signal clarity, uptime, and user safety. This is especially true in high-density urban zones and shared bands near 700/800 MHz, UHF, and VHF where overlapping transmissions and spurious emissions can compromise mission-critical communications. VHF in particular it’s often overlooked — yet notoriously difficult to manage due to its susceptibility to environmental noise, intermodulation, and long-range propagation effects.

Traditionally, Spectrum Monitoring in LMR has relied on reactive diagnostics, static thresholds, and manual surveys. These legacy tools are often slow to detect interference and offer limited insight into dynamic RF conditions.

What Is AI-Driven Real-Time Spectrum Sensing?

In traditional telecommunications, Spectrum Monitoring refers to the continuous monitoring of radio frequencies to detect active carriers, interference sources, and to ensure regulatory compliance toward sustaining or improving operational integrity of radio sites. It’s often rule-based, reactive, and manually interpreted.

However, in the AI lexicon, the term Spectrum Sensing has emerged as a more dynamic and intelligent alternative. It implies not just observing but actively interpreting and predicting radio frequency behavior using machine learning systems. The shift in terminology reflects a deeper transformation: Monitoring is static and descriptive, whereas Sensing is adaptive and predictive.


Traditional Spectrum Monitoring vs. AI-driven Real-Time Sensing

Scherm­afbeelding 2025-09-16 om 10.07.55


How AI Enhances LMR Spectrum Awareness

In an AI-driven approach, machine learning models—typically trained on signal spectrograms, IQ samples, or RF feature-sets exist to identify:

 

  • Unlicensed or rogue transmissions in protected bands
  • Spurious emissions from nearby electronics or faulty transmitters
  • Co-channel and adjacent channel interference patterns
     

AI-driven ML systems will not only detect anomalies but will also classify, learn, and forecast interference before it becomes disruptive. This proactive intelligence will mark a departure from legacy spectrum monitoring tools.

When embedded into a base station, repeater, or RF probe for example, trained AI models can likely:

 

  • Classify signal types: Voice vs. data, LMR vs. non-LMR, P25 vs. LTE
  • Detect anomalies: Sudden power spikes, distorted waveforms, overlapping signals
  • Predict interference zones: Based on traffic patterns and environmental data
     

These models can be trained on narrowband RF profiles such as P25, DMR, and NXDN, and potentially integrated into existing monitoring systems or cloud-based dashboards.

AI-Driven Spectrum Sensing: Lessons from 5G

At Mobile World Congress 2025, several key demonstrations showcased the growing role of AI in spectrum sensing:

DeepSig unveiled OmniSIG, an AI-based platform capable of real-time signal classification and interference localization within Open RAN environments. While originally designed for 5G, its core architecture is well-suited for adaptation to Land Mobile Radio (LMR) systems.

Keysight and InterDigital jointly demonstrated dynamic AI-enabled sensing using existing 3GPP signals.
 

AI-based spectrum sensing unlocks new capabilities for wireless systems, including 

  • Real-time detection of rogue or unlicensed transmissions
  • Identification of spurious emissions from nearby electronics or faulty transmitters
  • Classification of co-channel and adjacent channel interference patterns
     

The success of spectrum sensing in 5G highlights its potential to elevate LMR operations with smarter interference detection, dynamic spectrum use, and enhanced network stability.

Envisioned Benefits for Mission-Critical Radio Networks
 

  • Faster Incident Response: Operators can act immediately upon interference detection—no need to wait for complaints or drive tests.
  • Smarter Frequency Planning: AI can identify underused spectrum segments and recommend reallocation or load balancing.
  • Improved System Resilience: Enhanced situational awareness across remote sites helps prevent cascading failures.
     

Looking Ahead

As AI-based spectrum sensing continues to evolve, its adaptation to Land Mobile Radio (LMR) systems will redefine how critical networks detect interference, allocate spectrum, and maintain operational integrity. What began in 5G is now laying the foundation for smarter, more resilient radio systems—where sensing replaces monitoring, and intelligence becomes the new standard.

AI-based spectrum sensing is emerging as a transformative capability in next-generation wireless networks.

 

Disclaimer: The views expressed in this article are my own, shaped by my professional experience and ongoing AI learning journey, and do not represent the views of any organization with which I am affiliated.