Spectrum Sensing Based on the Statistical Test of Jarque-Bera for Different Modulation Schemes

This paper presents the results of spectrum sensing analysis to determine if a signal, measured by a cognitive user in an AWGN or Rayleigh channel comes from a primary user, or if it is composed of noise alone. The proposed method for spectrum sensing is based on statistical tests to identify if the received signal probability distribution has noise characteristics, or a different distribution that could classify the transmitter as a primary user. The results of Monte-Carlo simulations indicate that it is possible to identify transmission opportunities using spectrum sensing tests even for a low signal-to-noise ratio, around -23 dB with a detection probability above 0.9, up to 5 dB less than in the case of the energy detection, that is the most widely used spectrum sensing technique. In the simulations, the signals transmitted by the primary users are modulated in phase (PSK) and quadrature (QAM).