Recently, cyclostationarity (CS) based detection methods exploiting the autocorrelation periodicity property of the orthogonal frequency division multiplexing (OFDM) signals attracted a lot of attention. These detection methods are more complex than energy detection but they have better detection performance in low-SNR regimes. The drawback, however, is their extensive computational complexity. In this paper, we propose a computationally efficient spectrum sensing method for detecting unsynchronized OFDM signals in additive white Gaussian noise (AWGN). The proposed method exploits the second-order CS property of OFDM signals to set an adaptive threshold, which achieves the desired constant false alarm rate (CFAR) property. The complexity of the proposed method is significantly reduced compared with the scheme proposed in [22]. Monte-Carlo simulations illustrate that the performance of the proposed detector outperforms the classical energy detector (ED), the sliding window detector (SW) and Axell's detector in addition to guaranteeing a low sensing time.

A fast spectrum sensing for CP-OFDM cognitive radio based on adaptive thresholding

GINI, FULVIO;GRECO, MARIA;STINCO, PIETRO
2016-01-01

Abstract

Recently, cyclostationarity (CS) based detection methods exploiting the autocorrelation periodicity property of the orthogonal frequency division multiplexing (OFDM) signals attracted a lot of attention. These detection methods are more complex than energy detection but they have better detection performance in low-SNR regimes. The drawback, however, is their extensive computational complexity. In this paper, we propose a computationally efficient spectrum sensing method for detecting unsynchronized OFDM signals in additive white Gaussian noise (AWGN). The proposed method exploits the second-order CS property of OFDM signals to set an adaptive threshold, which achieves the desired constant false alarm rate (CFAR) property. The complexity of the proposed method is significantly reduced compared with the scheme proposed in [22]. Monte-Carlo simulations illustrate that the performance of the proposed detector outperforms the classical energy detector (ED), the sliding window detector (SW) and Axell's detector in addition to guaranteeing a low sensing time.
2016
Berbra, Kamel; Barkat, Mourad; Gini, Fulvio; Greco, Maria; Stinco, Pietro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/827473
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