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- W2996565376 abstract "Next generation networks are expected to operate in licensed, shared as wellas unlicensed spectrum to support spectrum demands of a wide variety ofservices.Due to shortage of radio spectrum, the need for communicationsystems(like cognitive radio) that can sense wideband spectrum and locatedesired spectrum resources in real time has increased.Automatic modulationclassifier (AMC) is an important part of wideband spectrum sensing (WSS) as itenables identification of incumbent users transmitting in the adjacent vacantspectrum.Most of the proposed AMC work on Nyquist samples which need to befurther processed before they can be fed to the classifier.Working with Nyquistsampled signal demands high rate ADC and results in high power consumption andhigh sensing time which is unacceptable for next generation communicationsystems.To overcome this drawback we propose to use sub-nyquist sample basedWSS and modulation classification. In this paper, we propose a novelarchitecture called SenseNet which combines the task of spectrum sensing andmodulation classification into a single unified pipeline.The proposed method isendowed with the capability to perform blind WSS and modulation classificationdirectly on raw sub-nyquist samples which reduces complexity and sensing timesince no prior estimation of sparsity is required. We extensively compare theperformance of our proposed method on WSS as well as modulation classificationtasks for a wide range of modulation schemes, input datasets, and channelconditions.A significant drawback of using sub-nyquist samples is reducedperformance compared to systems that employ nyquist sampled signal.However,weshow that for the proposed method,the classification accuracy approaches toNyquist sampling based deep learning AMC with an increase in signal to noiseratio." @default.
- W2996565376 created "2019-12-26" @default.
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- W2996565376 date "2019-12-11" @default.
- W2996565376 modified "2023-10-02" @default.
- W2996565376 title "SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network." @default.
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