The LEO Satellite Spectrum Sensing Based on the Multi-resolution Signal Decomposition

In the LEO satellite spectrum sensing, choosing a proper frequency resolution for detecting the energy is very crucial. A too high resolution fails to detect spectrum holes which probably exist, while a too low one adds the computation and misjudges the jitter in frequency-domain as holes. Moreover, because backward-link sensing data need to be transferred to ground stations to do synthesis and detection, it is obvious that the smaller the quantity of data is, the easier for it to be transmitted. In order to improve the detecting accuracy and to reduce transmitting information, this paper proposes a LEO satellite spectrum sensing based on the multi-resolution signal decomposition. The simulation of this technology concludes that in backward sensing data transmission, multi-resolution signal decomposition can keep well shape of power spectral density function while largely decreasing spectrum sensing data. Besides, in the process of spectrum hole detecting, compared to fixed-resolution detecting, this technology can raise greatly the convergence rate of spectrum hole location and meantime reduce statistical error of holes.