Modulation format identification assisted by sparse-fast-Fourier-transform for hitless flexible coherent transceivers

Abstract

For hitless flexible coherent transceivers based next-generation agile optical network, efficient modulation format identification (MFI) is an essential element in digital signal processing (DSP) flow at the receiver-side (Rx). In this paper, we propose a blind and fast MFI scheme with high identification accuracy at low optical signal-to-noise ratio (OSNR) regime. This is achieved by first raising the signal to the 4th power and calculate the peak-toaverage power ratio (PAPR) of the corresponding spectra to distinguish 32 quadrature amplitude modulation (QAM) from quadrature phase shift keying (QPSK), 16 and 64QAM signals. Then, followed by iterative partition schemes to remove signals with phase 3 , 4 4 π π ± ± (or QPSK-like phases) based on the signal amplitudes, the PAPR of the remaining signals is calculated to distinguish the other three formats. Additionally, by frequency offset (FO) pre-compensation, the spectrum can be obtained using sparse-fast-Fourier-transform (S-FFT), which greatly reduces the total complexity. The MFI performance is numerically and experimentally investigated by 28 Gbaud dual-polarization (DP) coherent optical back-toback (B2B) and up to 1500 km standard single mode fiber (SSMF) transmission system using QPSK, 16QAM, 32QAM, and 64QAM. Results show that high identification accuracy can be achieved, even when OSNR is lower than that required for the 20% forward error correction (FEC) threshold of-2 BER=2 10 × for each format. Furthermore, fast format switching between 64QAM-32QAM and 32QAM-16QAM are demonstrated experimentally for B2B scenario and 900 km SSMF with the proposed MFI technique, respectively.

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

“…For the proposed scheme, the required real multiplications, real additions and comparisons were 6246, 6286 and 492,401, respectively. It should be noted that the complexity of the comparators was much lower than those related to multipliers and adders [29]. The proposed scheme and the scheme using DNN were both based on amplitude histogram.…”

“…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).…”

“…However, in order to obtain the desired optimal performances, these schemes require a large number of training samples, high computational resources and a complex training process. Meanwhile, if link conditions, such as distance, or other parameters change, machine learning models may need to be retrained [29]. Therefore, a prior-training-free and low-complexity MFI scheme is highly desirable for practical deployment.…”

Modulation Format Identification in Mode Division Multiplexed Optical Networks

“…Besides, other MFI methods based on binary decision tree have been investigated in literature. This includes spectrum analysis [22], [23], amplitude deviation analysis [24], the entropy of the normalized amplitude histogram [25], and the distribution of amplitude and phase [26] of the received signal.…”

Blind Modulation Format Identification Using the DC Component

“…The digital frequency-offset loading technique at the transmitter side was reported in [16]. MFI is also performed based on iterative partition schemes and calculating the peak to average power ratio of the corresponding spectra [17]. But iterative partitions are not needed in our scheme.…”