December 15, 2022 – KIRKLAND, WA — Spectrum Effect® announced today the introduction of a new Object Detection ML Model in Spectrum-NET for handling the most challenging RF interference environments. With the object detection ML model, Spectrum-NET is now able to separate, characterize and classify each of multiple RF interference sources that are simultaneously affecting the same channel.
Spectrum-NET is the industry’s leading solution for the automated ML-driven analysis of RF interference, enabling operators to rapidly and effectively mitigate sources of RF interference.
“Mobile operator networks in urban environments in some geographies suffer from high amounts of severe RF interference coming from multiple sources, especially in 3GPP frequency bands 3, 5, 8 and 28,” commented Spectrum Effect Chief Technology Officer, Eamonn Gormley. “Our new Object Detection ML Model provides clear guidance to our operator customers on how to resolve each of these sources even in the harshest environments.”
About Spectrum Effect
Spectrum Effect’s mission is to solve the most challenging and costly problems in the wireless industry through innovation and automation. With R&D located in Kirkland, Washington, USA, and San Pedro Garza Garcia, Mexico, the Spectrum Effect team is passionate about creating disruptive technologies, engineering excellence, and enhancing the experience of mobile operators. Spectrum Effect has created Spectrum-NET, the industry’s leading solution for the automated analysis of RF interference. Protected by 30 issued patents, Spectrum-NET performs machine learning-driven analysis of RF interference across all RF bands and operates seamlessly across multi-vendor 5G NR, LTE and UMTS cells within mobile networks continually. With Spectrum-NET, operators across the globe are rapidly addressing RF interference, improving network KPIs, surgically deploying their field assets, gaining insights into spectral efficiency, and saving significant OPEX and CAPEX. www.spectrumeffect.com.
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