2019
DOI: 10.1088/1742-6596/1378/4/042092
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Dynamic Spectrum Sensing with Automatic Modulation Classification for a Cognitive Radio Enabled NomadicBTS

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Cited by 5 publications
(5 citation statements)
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“…The optimal AMR model obtained in this study is based on the compact FOS features that will form a CR component for the real-time deployment of NomadicBTS architecture to achieve dynamic spectrum sensing [26]. Similar efforts on the use of statistical features and CR for spectrum sensing have also been reported in the literature [27]- [29].…”
Section: Resultsmentioning
confidence: 69%
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“…The optimal AMR model obtained in this study is based on the compact FOS features that will form a CR component for the real-time deployment of NomadicBTS architecture to achieve dynamic spectrum sensing [26]. Similar efforts on the use of statistical features and CR for spectrum sensing have also been reported in the literature [27]- [29].…”
Section: Resultsmentioning
confidence: 69%
“…The NomadicBTS architecture has two vital sub-modules with the front-end housing the SDR hardware while the SDR software operates on a personal computer (PC) at the back-end [26]. The architecture was extended in our study reported in [27] by incorporating CR capability with the AMR-based spectrum sensing model in the NomadicBTS architecture, where four (4) modulation schemes were considered and employed, namely, amplitude modulation (AM), Gaussian minimum shift key (GMSK), frequency modulation (FM) and (iv) noise (no-modulation).…”
Section: Methodsmentioning
confidence: 99%
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“…An approach to incorporate opportunistic spectrum sensing into the Nomadic Base Transceiver Station (BTS) architecture, a hybrid Automatic Modulation Classification (AMC) based spectrum sensing model was developed in [27]. Selected analogue and second generation (2G) digital modulation methods were evaluated, and the accuracy of the best model obtained will allow for the most accurate detection of spectrum holes within the bands under consideration].…”
Section: Related Workmentioning
confidence: 99%