Exploiting Periodical Peaks of Autocorrelation of Pilot-Added OFDM Signals for Enhanced Spectrum Sensing Algorithms

Inhalt:
Spectrum sensing is a challenging task in cognitive radio. A lot of research work in spectrum sensing aims to improve the performance of algorithms. In this paper, we present our exploitation on periodical peaks of autocorrelation in time-domain for pilot-added OFDM signals. Digital video broadcasting terrestrial (DVB-T) signal is taken as an example for the exploitation. The results of this exploitation lead to an approach to enhanced spectrum sensing including detection and classification. Therefore, we propose two enhanced sensing algorithms for DVB-T signal, namely, periodical peaks of autocorrelation (PPA)- based detection with Neyman Person (NP) solution and with maximum rate combination (MRC), respectively. It is found that PPA-based detection algorithms outperform the previous existing detection algorithms which are based on time-domain symbol cross-correlation (TDSC) by about 1dB to 2.5dB. Moreover, the simulation results show that the proposed algorithms are low sensitivity to noise uncertainty and work well in different environments.