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AI-based RF Awareness for Private Wireless Networks

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Pages: 10

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 involves monitoring and analyzing the RF environment in which the private network operates, looking for sources of interference and/or spurious RF sources or monitoring. It addresses both the need to improve network performance and increase network security.

RF detection has traditionally been carried out manually and has tended to be reactive, expensive and subject to delays or error from a myriad of externalities. However, next-generation solutions 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.

This white paper, which was sponsored by DeepSig, discusses the benefits of using next-generation RF awareness solutions, such as OmniSIGĀ® from DeepSig, when deploying private wireless networks. The white paper also discusses the benefits of private wireless networks and the opportunity for their deployment on various licensed and unlicensed/shared spectrum bands.


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