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arXiv 1901.11368eess.SP

Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

Gihan Mendis, Jin Wei, Arjuna Madanayakey, Soumyajit Mandalz

英文摘要:

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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