Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

Cognitive Radio is an advanced enabling technology for efficient utilization of vacant spectrum due to its ability to sense the spectrum environment. Various detection methods have been proposed for spectrum sensing, which is the key function in implementing cognitive radio. However most of the existing methods put their interests in detecting TV signal and wireless microphone signals. In this paper, we explore the periodicity of the equally spaced pilot subcarriers in OFDM signal. Simulations in various fading environments show that the proposed cyclostationarity based detection method works well for OFDM signal.