Autocorrelation-Based Spectrum Sensing for OFDM Signals over Multipath Fading Channel in Cognitive Radio Systems

Abstract

In this paper, a spectrum-sensing method is proposed which exploits the combination of a comb filter and autocorrelation function. The proposed method improves the detection performance in severe noise environments, where a cyclic prefix (CP)-based orthogonal frequency division multiplexing (OFDM)transmitted signal is considered. In our proposed method, the primary user information is not required for spectrum sensing. The detection performance is measured over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels for different digital modulations. A comprehensive evaluation by simulation shows that the proposed method significantly outperforms the conventional schemes at a low signal-to-noise ratio (SNR).

Spectrum Sensing Based on Higher Order Statistics for OFDM Systems over Multipath Fading Channels in Cognitive Radio

“…Figures 4-7 show that when the CP size decreases, the P d of the proposed spectrum sensing slightly decreases. It happens due to the ISI caused by the multipath channel, which is not mitigated by a short CP size [16]. P d is excellent under 64-QAM for various CP sizes in severe noise environments, which is very important for OFDM-based systems.…”

“…The proposed method is compared with the CPautocorrelation-based spectrum-sensing method [15] and autocorrelation with a filter-based spectrumsensing method [16] in Figure 9. The simulation parameters for the CP-autocorrelation-based sensing method are an FFT size (N ) of 32, a CP ratio (N c ) of 1/4, digital 16-QAM, and a P fa of 0.05.…”

“…The detection performance of OFDM-transmitted signals is unsatisfactory in severe noise environments. Furthermore, a comb filter was used prior to the autocorrelation for SNR improvement and large fast Fourier transform (FFT) sizes were used in [16]. The detection performance of OFDM-transmitted signals is not very good in severe noise environments.…”