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How more advanced, AI based RF detection can be used to detect interfering or unauthorized RF sources

Arlington, Virginia — August 3, 2022DeepSig—experts in artificial intelligence (AI) and machine learning (ML) for wireless communications—published “AI based RF Awareness for Private Wireless Networks.” Authored by iGR, a leading wireless industry analyst, this white paper explores how enterprise and government facilities can leverage AI and ML solutions to amplify RF detection and operational performance in private wireless networks.

Private wireless network success metrics are determined, either in whole or in part, by the performance of the network and on network security.  Similarly, network security is crucial for most private wireless deployments.

The paper explains how more advanced, AI based RF detection can be used to detect interfering or unauthorized RF sources, addressing the need to improve network performance, increase network detection faster, more accurately and more cost effectively than traditional methods.

DeepSig’s next generation solution, OmniSIG, uses machine learning and automated, real time RF monitoring to detect real world RF conditions in private wireless networks. The key operational 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 tool sets.

Private wireless networks are relatively new. Solutions such as OmniSIG have the ability to rapidly detect known and unknown wireless signals and anomalies in real time, allowing centralized or remote staff to simultaneously relate the wireless environment to service conditions.  This leads to greater accuracies in operational environments. The technology is well understood and many organizations have deployed proof of concept and trial networks, with more to come over the next few years.

The white paper “AI based RF Awareness for Private Wireless Networks” is available for download here.


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