High Order Modulation Format Identification Based On Comressed Sensing

In the elastic dynamic optical fiber communication system, it is very important to recognize the modulation format of the communication signal.In this paper, according to the digital communication signal modulation technology, combined with compressed sensing technology, proposed a modulation format identification based on compressed sensingFor sparse signals or compressible signals, compressive sensing theory sample the signals use a frequency far below the Nyquist sampling rate. So reduce the sampling and dynamic range requirement for analog to digital converter in receiving end, and relieve the pressure on the digital end of a large amount of data processing.We propose a method of modulation format identification (MFI) based on compressed sensing (CS) using high-order cyclic cumulant combined with binary tree classifier. Through computing the fourth-order cyclic cumulant of the pretreated band signal which is obtained by compressed sensing with sampling rate much less than the Nyquist sampling value, the feature vector for classification is extracted. Simulation are carried out in the optical coherent fiber communication system with different modulation formats of M-PSK and M-QAM. The results indicate that this method can identify these modulation formats correctly and efficiently. Meanwhile, the proposed method is insensitive to laser phase noise and signal noise.Modulation format signal recognition based on compressed sensing and high order. Based on the energy value of the signal, the higher order accumulated value is used to construct the characteristic parameters of the modulation format identification.our main contribution is to reconstruct the cyclic spectrum of a sparse signal directly from sub-Nyquist-rate compressive samples, without having to recover the signal itself. The simulation results indicate that this method can effectively realize signal detection for modulation format identification in low OSNR conditions. In addition, on the basis of CS model, it gives an extraction method for cyclic spectrum feature which based on binary iteration and also combines with binary tree classifier for five kinds of common signal modulation recognition. This technique utilizes compressed sensing with sampling rate much less than the Nyquist sampling rate and binary tree classifier to enable low-cost identification at the receivers as well as at the intermediate network nodes without requiring any prior information from the transmitters.