A Statistical Knowledge Autocorrelation-Based Algorithm for Spectrum Sensing of OFDM Signals in Channels With Frequency Offset

This paper presents a novel autocorrelation-based algorithm that uses statistical knowledge to detect orthogonal frequency division multiplexing signals in channels where frequency offset is present. The algorithm may be viewed as a significant improvement over other types of autocorrelation algorithms that appear in literature that lead to false alarm due to the hardware impairment of frequency offset. The algorithm works by making an unbiased estimate of the square of an autocorrelation coefficient and from that deduces an appropriate probability density function for the phase angle of the complex test statistic and thereby palliating the effect of phase distortion introduced by the frequency offset. It is shown that the algorithm presented in this paper can be implemented on a testbed, as well as overcome simulations that have been specifically designed to have worst case frequency offset phase distortion conditions.