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Automatic identification of modulated signals is an important technology of power line communication. Aiming at the problems of low signal recognition rate and characteristic parameter extraction in power line communication channel, this paper uses amplitude variance value of wavelet transform and higher order cumulant as the identification parameter, and designs a signal recognizer based on improved support vector machine. Under the condition of power line channel environment, the recognizer of this paper is less than the existing recognition method in the computational complexity, and has good robustness to power line noise. At the same time, it avoids the shortcomings of traditional neural network such as under-learning and over-learning. The simulation results show that when the SNR is 5 dB, power line communication signals through the recognizer, which the correct identification rate can reach 91%.

Algorithms for Modulation Recognition of Narrowband Power Line Carrier Communication Signals