Multichannel Software Defined Radio with Spectral Decision via Centralized Artificial Intelligence

Vlad Fernoaga, R. Curpen, Cosmin Nutiu, F. Sandu
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Abstract

The present paper aims to bring Artificial Intelligence (AI) in Software Defined Radio (SDR). A multichannel spectrum sensing problem, extended to a long-term spectral occupancy observation, enabled the authors to derive a “vertical” per-channel machine learning model that was tested in an integrated National Instruments (NI) environment – Ettus/NI USRP (Universal Software Radio Peripherals) service-driven, top-down, by LabVIEW. The proof-of-concept was based on a simple 8 channels PMR (Private Mobile Radio) use-case.
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基于集中式人工智能的频谱决策多通道软件无线电
本文旨在将人工智能(AI)引入软件定义无线电(SDR)。多通道频谱感知问题,扩展到长期频谱占用观察,使作者能够推导出一个“垂直”的每通道机器学习模型,该模型在集成的国家仪器(NI)环境中进行了测试- Ettus/NI USRP(通用软件无线电外设)服务驱动,自上而下,由LabVIEW。概念验证是基于一个简单的8通道PMR(专用移动无线电)用例。
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