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

Abstract

In this work, a prior-training-free and low-complexity modulation format identification (MFI) scheme, based on amplitude histogram distributions, was proposed and demonstrated, both numerically and experimentally, for autonomous digital coherent receivers. In the proposed scheme, after having performed power normalization, incoming polarization division multiplexed (PDM) signals were classified into QPSK, 8QAM, 16QAM, 32QAM and 64QAM signals, according to their ratios. Ratios were defined according to specific features of their amplitude histograms. The proposed MFI scheme used only amplitude information. As such, it was insensitive to carrier phase noise. Furthermore, the proposed scheme did not require any prior information, such as optical signal-to-noise ratio (OSNR). The performance of the proposed MFI scheme was numerically verified using 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM signals. The numerical simulation results showed that the proposed scheme was able achieve a 100% correct identification rate for all five modulation formats when their OSNR values were higher than the thresholds corresponding to the 20% FEC correcting bit error rate (BER) of 2.4 × 10−2. To further explore the effectiveness of the proposed MFI scheme, proof-of-concept experiments in 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM transmission systems were also undertaken, which showed that the proposed scheme as robust against fiber nonlinearities. To explore the scheme’s feasibility for use in practical transmission systems, the computational complexity analysis of the proposed scheme was conducted. It showed that, compared with relevant MFI schemes, the proposed MFI scheme was able to significantly reduce the computational complexity.

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

“…Although there are nine amplitude levels for 64QAM, its corresponding amplitude histogram does not, however, clearly illustrate nine distinguishable levels in the presence of noise. Since the associated probability for the sixth amplitude level of 64QAM is highest [3], N 5 , representing the number of symbols within the range of (A 5 ~B5 ), can thus be calculated to extract the feature for 64QAM. N 1 , N 2 , N 3 , N 4 and N 5 mainly represent the features located in the front and middle of the relevant histograms, whilst N 6 for 128QAM extracts the feature located in the last part of the histogram.…”

“…Meanwhile, the optical network is required to meet heterogeneous and dynamic demands for supporting diverse data services, such as 5G, cloud computing, big data and the internet of things by optimizing the allocation of the bandwidth and modulation format. As a direct result, the optical network is evolving from fixed network architectures to flexible and elastic ones [2,3]. In elastic optical networks (EONs), according to different channel characteristics and various data services, the involved transceivers can dynamically adjust the modulation formats utilized for encoding the optical signals, which raises new demands on digital coherent receivers.…”