Modulation classification based on spectrogram

1 .INTRODUCTIONThe ai mof modulation classification ( MC) is toiden-tifythe modulationtype1]of a communicationsignal .It plays ani mportant rolein many cooperative or non-cooperative communication applications such as soft-ware radio,intelligent modem,andelectronic surveil-lance system2].Inthe past ,such workrelied heavilyon human operators ,which becomesless practical dueto the increasing density of the frequency spectrumand the increasing complexity and diversity of themodulation type…

Modulation classification based on spectrogram

The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram based modulation classification methods are proposed. Their recognition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment like features is robust to frequency offset while the other two, which make use of principal component analysis (PCA) with different transformation inputs, can achieve satisfactory accuracy even at low SNR (as low as 2 dB). Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.