In recent years, there are more and more modulation formats in wireless and optical communication systems [
2,
3]. The modulation format identification (MFI) in optical fiber communication systems has attracted more and more scholars’ interest. One method is based on the peak to average power ratio (PAPR) of received signal [
4–
6]. It identifies the modulation format of the signal by collecting the distribution of the value of the power of the signal, but it only identify some existing fixed modulation formats. Another method is based on asynchronous delay tapped sampling (ADTS), two acquisition cards collect the same optics signal. The delay time is fixed [
7–
11]. The amplitude of the acquisition is
Pi and
Qi, respectively. The distribution of statistical
Pi and
Qi can be made into a two-dimensional amplitude histogram. The
PQ two-dimensional histogram of the optical signals with different modulation formats and different bit rates is also different. So different modulation formats are identified by analyzing the differences of these histograms. But it requires two high-speed acquisition cards to sample the same optical signal asynchronously, so the cost is higher. The other method is to use deep neural network based on the asynchronous amplitude histograms of the received signal [
12–
15]. The neural network was trained with asynchronous amplitude histograms to identify the modulation formats. But it needs a lot of number of input data and the iterative algorithm of the neural network is too complex.