Blind modulation format identification based on improved PSO clustering in a 2D Stokes plane

Abstract

Blind modulation format identification (MFI) is indispensable for correct signal demodulation and optical performance monitoring in future elastic optical networks (EON). Existing MFI schemes based on a clustering algorithm in Stokes space have gained good performance, while only limited types of modulation formats could be correctly identified, and the complexities are relatively high. In this work, we have proposed an MFI scheme with a low computational complexity, which combines an improved particle swarm optimization (I-PSO) clustering algorithm with a 2D Stokes plane. The main idea of I-PSO is to add a new field of view on each particle and limit each particle to only communicate with its neighbor particles, so as to realize the correct judgment of the number of multiple clusters (local extrema) on the density images of the s 2 − s 3 plane. The effectiveness has been verified by 28 GBaud polarization division multiplexing (PDM)-BPSK/PDM-QPSK/PDM-8QAM/PDM-16QAM/PDM-32QAM/PDM-64QAM simulation EON systems and 28 GBaud PDM-QPSK/PDM-8QAM/PDM-16QAM/PDM-32QAM proof-of-concept transmission experiments. The results show that, using this MFI scheme, the minimum optical signal-to-noise ratio (OSNR) values to achieve 100% MFI success rate are all equal to or lower than those of the corresponding 7% forward error correction (FEC) thresholds. At the same time, the MFI scheme also obtains good tolerance to residual chromatic dispersion and differential group delay. Besides that, the proposed scheme achieves 100% MFI success rate within a maximum launch power range of − 2 ∼ + 6 dBm. More importantly, its computational complexity can be denoted as O ( N ) .

Modulation Format Identification Based on Multi-Dimensional Amplitude Features for Elastic Optical Networks

“…The previously reported MFI schemes for EON can be classified into data-aided and non-data-aided schemes [5][6][7]. Since additional pilot information is introduced in the transmitter, the computational complexity of the data-aided schemes [8][9][10] is thus low with an additional cost of a reduced spectral efficiency.…”

Modulation format identification based on the mean-shift algorithm

“…In recent years, there has been a proliferation of modulation format identification methods based on feature extraction. Common methods include those based on signal amplitude and phase accumulation [1][2][3], machine learning/deep learning methods [4][5][6][7], methods based on the Stokes plane or other fitting two-dimensional planes [8][9][10], methods based on clustering algorithms [11][12], and principal component analysis [13][14]. In the above method, the methods based on signal amplitude-phase accumulation or machine learning/deep learning require the collection of a great quantity of signal data samples as training data for feature calculation or dataset training in order to obtain signal features for identification.…”

Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

“…The previously-reported MFI schemes for optical fiber communications were roughly classified into the following three categories: (1) data-aided schemes [9][10][11], in which additional pilot information is introduced and the computational complexity of the MFI scheme is low (at the cost of reduced spectral efficiency); (2) schemes based on Stokes space [7,8,[12][13][14][15][16][17][18][19][20][21][22][23], which are not sensitive to carrier phase noise, frequency offset or polarization mixing; (3) schemes based on signal characteristics arising from constant modulus algorithm (CMA) equalization [24][25][26][27][28][29][30][31][32][33], which are based on CMA-equalized signals and do not require any space mapping. Meanwhile, CMA can also compensate for residual chromatic dispersion (CD) and polarization mode dispersion (PMD).…”