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- W3016502135 abstract "Spectrum sensing is one among the major functions of Cognitive radio systems. Meanwhile, wideband spectrum sensing is a challenging issue with these systems. Conventional wideband techniques require analog-to-digital converters operating at Nyquist sampling rates. Sub-Nyquist sampling or compressed sensing techniques, however, require low rate analog-to-digital converters. In order to avoid interference to primary user transmissions, the cognitive users should perform continuous spectrum sensing and identify the active primary bands even at low-signal-to-noise ratios. In this paper, we propose a sub-Nyquist wideband spectrum sensing technique using multicoset sampling and revised orthogonal matching pursuit (OMP). Conventional orthogonal matching pursuit algorithm uses n iterations to recover n-sparse signal. Extending the iterations beyond n further improves the performance, for instance, extended OMP (OMP α) and sparsity unaware OMP (OMP ∞) achieves a better detection capability by increasing the number of iterations beyond n. We analyse the performance of multicoset sampling based wideband spectrum sensing with various OMP algorithms." @default.
- W3016502135 created "2020-04-24" @default.
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- W3016502135 date "2020-01-01" @default.
- W3016502135 modified "2023-09-25" @default.
- W3016502135 title "Wideband spectrum sensing using multicoset sampling and extended orthogonal matching pursuit" @default.
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- W3016502135 doi "https://doi.org/10.1063/5.0004051" @default.
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