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AI-based RF Awareness for Private Wireless Networks

Enterprise and government facilities considering private LTE/5G networks are generally in the learning and discovery phase of what they need and the best approach to meet their needs. As new private wireless networks are deployed, the need to maintain network performance, lower TCO, implement and manage security, and protect corporate assets will require new practices and tools.


RF detection addresses both the need to improve network performance and increase network security – this means monitoring and analyzing the RF environment in which the private network operates, looking for sources of interference and/or spurious RF sources or monitoring. RF detection has traditionally been carried out manually, with the operator using RF monitoring equipment to look for rogue or unauthorized RF signals – this process tends to be reactive, expensive and subject to delays or error from a myriad of externalities.


Next-generation solutions, such as OmniSIG® from DeepSig, use AI machine learning and automated, real time RF monitoring to detect real- world RF conditions and detect interfering or unauthorized RF sources many times faster, more accurately, and at lower cost than traditional approaches. OmniSIG rapidly detects known and unknown wireless signals and anomalies in real time, allowing centralized or remote staff to simultaneously
relate the wireless environment to service conditions. OmniSIG’s operational improvements and greater accuracies have much lower costs than traditional methods.

The benefits of OmniSIG include:

  • ML-driven spectrum awareness and sensing using trained models to classify RF signals in real time
  • For enterprises, OmniSIG can differentiate and identify signals within the whole environment, both within the building and across the campus
  • Scans wide bandwidths quickly and efficiently and can differentiate signals using the same waveforms
  • Operates with a wide range of generally available radio devices and industry-leading test and measurement toolsets.


Private wireless networks are relatively new but the technologies are well understood and many enterprises, vendors, municipalities and organizations have deployed proof-of-concept and trial networks, with many more to come in the next few years. iGR forecasts that the network spending (network equipment, installation and integration) opportunity in the U.S. alone just for CBRS will reach $5.1 billion in 2026. Globally, iGR estimates the private wireless network opportunity to be in excess of $9 billion in 2026. These estimates are only to deploy the private wireless networks and do not include the opportunity for developing and deploying new applications and services.

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