Modulation Format Identification Scheme Research Articles

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

A modulation format identification (MFI) scheme based on multi-dimensional amplitude features is proposed for elastic optical networks. According to the multi-dimensional amplitude features, incoming polarization division multiplexed (PDM) signals can be identified as QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals using the k-nearest neighbors (KNNs) algorithm in the digital coherent receivers. The proposed scheme does not require any prior training or optical signal-to-noise ratio (OSNR) information. The performance of the proposed MFI scheme is verified based on numerical simulations with 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM/-128QAM signals. The results show that the proposed scheme can achieve 100% of the correct MFI rate for all six modulation formats when the OSNR values are greater than their thresholds corresponding to the 20% forward error correction (FEC) related to a BER of 2.4 × 10−2. Meanwhile, the effects of residual chromatic dispersion, polarization mode dispersion and fiber nonlinearities on the proposed scheme are also explored. Finally, the computational complexity of the proposed scheme is analyzed, which is compared with relevant MFI schemes. The work indicates that the proposed technique could be regarded as a good candidate for identifying modulation formats up to 128QAM.

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

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 Technology Based on a Searching Cluster Boundary Clustering Algorithm

In this study, a modulation format identification (MFI) scheme based on a searching cluster boundary (SCB) algorithm is designed for space optical communication. Particularly, we design a clustering algorithm based on local low-density samples as the boundaries of clusters and use the boundary to distinguish different clusters; this is unaffected by the distribution of samples in the clusters. The SCB algorithm was applied to MFI, and a verification experiment for space optical communication was performed. The experimental results show that the SCB algorithm achieves good results with clusters of different shapes and the designed MFI scheme can accurately identify modulation formats of different orders with few symbols.

Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.

Blind and low-complexity modulation format identification based on signal envelope flatness for autonomous digital coherent receivers.

Modulation format identification (MFI) is a critical technology for autonomous digital coherent receivers in next-generation elastic optical networks. A novel and simple MFI scheme, to the best of our knowledge, based on signal envelope flatness is proposed without requiring any training or other prior information. After amplitude normalization and partition, the incoming polarization division multiplexed (PDM) signals can be classified into quadrature phase shift keying (QPSK), 8 quadrature amplitude modulation (QAM), 16QAM, and 64QAM signals according to envelope flatnesses R1, R2, and R3 of signals in different amplitude ranges. The feasibility of the proposed MFI scheme is first verified via numerical simulations with 28 GBaud PDM-QPSK/-8QAM/-16QAM/-64QAM signals. Only by using 4000 symbols can the proposed MFI scheme achieve a 100% correct identification rate for the four modulation formats over a wide optical signal-to-noise ratio (OSNR) range. Proof-of-concept experiments among 28 GBaud PDM-QPSK/-8QAM/-16QAM systems under back-to-back and long-haul fiber transmission links are implemented to further demonstrate the effectiveness of the proposed MFI scheme. The experimental results show that the proposed MFI scheme can obtain a 100% correct identification rate when the OSNR value of each modulation format is higher than the threshold corresponding to 7% FEC and is resilient towards fiber nonlinearities. More importantly, the proposed MFI scheme can significantly reduce computational complexity.

Modulation format identification using the Calinski-Harabasz index.

Modulation format identification (MFI) is a key technology in optical performance monitoring for the next-generation optical network, such as the intelligent cognitive optical network. An MFI scheme based on the Calinski-Harabasz index for a polarization-division multiplexing (PDM) optical fiber communication system is proposed. The numerical simulations were carried out on a 28 Gbaud PDM communication system. The results show that the required minimum optical signal-to-noise ratio values of each modulation format to achieve 100% identification accuracy are all equal to or lower than their corresponding 7% forward error correction thresholds, and the proposed scheme is robust to residual chromatic dispersion. Meanwhile, the proposed scheme was further verified by 20 Gbaud PDM-QPSK/16QAM/32QAM long-haul fiber transmission experiments. The results show that the scheme has a good reliability when fiber non-linear impairments exist. In addition, the complexity of the scheme is significantly lower than that of other clustering-based MFI schemes.

Joint modulation format identification and OSNR estimation method based on trajectory information analysis

We propose a joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation method via analyzing the trajectory information of the received adjacent symbols. A uniform grid in the first quadrant of constellation diagram is designed for converting the trajectory information of the received adjacent symbols to the adjacent matrix of corresponding mapped graph structure. Then, we translate the OSNR-independent MFI scheme to an appropriate canonical vector searching optimization problem via improving on the classical cosine similarly algorithm, where the eigenvector associated with the largest eigenvalue of the adjacent matrix is served as discriminated-feature to identify five commonly-used modulation format types, i.e., polarization division multiplexing (PDM)-quadrature phase shift keying (QPSK), PDM=8 quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM, and PDM-64QAM. After exact modulation format type is obtained, we build the relationship between the second-largest eigenvalue of the adjacent matrix and the OSNR value via polynomial regression method, and the least-squares principle (LSP) is utilized to find the best fit line for obtaining the optimal polynomial coefficient. The proposed method is verified by 28 GBaud PDM-QPSK, PDM-8QAM, PDM-16QAM, PDM-32QAM, and PDM-64QAM optical signal numerical simulations, and the results show that could achieve excellent performance on various parameter analysis. Besides, a proof-of-concept experiment is also conducted. More specifically, the proposed method only need one training process and one matrix decomposition, which would provide a superior convenience for future optical performance monitoring.

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

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 s2-s3 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).

Low-complexity modulation format identification scheme via graph-theory in digital coherent optical receivers

Efficient modulation format identification (MFI) scheme is necessary for the flexible digital coherent transceivers in the next-generation elastic optical networks. In this paper, an innovative graph-theory based MFI scheme is presented, which transfers the trajectory and coordinate information of adjacent received symbols to the graph domain. The proposed MFI scheme contains two crucial stages, i.e. discriminated-feature extracted stage and the minimal acute-angle comparison stage. In the discriminated-feature extracted stage, a uniform identify grid model is established in the first quadrant of the constellation diagram, and a concise graph structure is conducted according to the coordinate and trajectory information of the adjacent received symbols. The eigenvector associated with the largest eigenvalue is selected as the discriminated-feature of the corresponding modulation format signal. In the minimal acute-angle comparison stage, the modulation format type of unknown signal is identified by calculating the acute-angle between the discriminated-features of the canonical modulation formats signals and the unknown one. The effectiveness of the proposed MFI scheme is demonstrated via 28 GBaud numerical simulations with polarization-division multiplexing (PDM)-quadrature phase shift keying (QPSK), PDM-8quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM and PDM-64QAM signals. The results show that high identification accuracy can be achieved over wide optical signal-to-noise ratio (OSNR) ranges. Meanwhile, we also discuss the influence of the residual chromatic dispersion (CD), launching optical powering, different group delay (DGD) to the proposed MFI scheme. Finally, the proposed MFI scheme is further verified by 20 GBaud PDM-QPSK/8QAM/-16QAM/-32QAM long-haul transmission experiments. The proposed MFI scheme with low computational complexity would certainly provide a superior convenience to the future elastic optical networks.

Relative entropy-based modulation format identification for coherent optical communication system

Modulation format identification (MFI) is widely used in elastic optical networks, such as optical performance monitoring and signal compensation. We propose an MFI scheme, through extraction of the amplitude probability distribution feature of the received signals and an analysis of the relative entropy, which is also known as the Kullback–Leibler divergence (KL). The modulation formats (MFs) can be distinguished via calculation of the KL value. Through this method, four commonly used transmission formats were investigated, namely, polarization-division multiplexing (PDM)-QPSK/16QAM/32QAM and 64QAM. A simulation transmission model was created, and the results were analyzed. The simulation results show that the MFs investigated can be distinguished when the optical signal-to-noise ratio (OSNR) is less than or equal to the corresponding theoretical 7% forward error correction (FEC) limit (bit error ratio = 3.8 × 10 − 3). In addition, two important factors, i.e., fiber nonlinearity and sample size, were investigated in relation to the proposed method. The results show that the method exhibits a certain tolerance to fiber nonlinearity and can be used to identify the MF at the FEC threshold even when the sample number is 1000. Furthermore, the experimental verifications were conducted to ensure the practicality of the proposed method.

Modulation Format Identification Using Graph-Based 2D Stokes Plane Analysis for Elastic Optical Network

We propose a graph-based modulation format identification (MFI) scheme for elastic optical network (EON), which exploits the trajectory information of the adjacent received symbols to identify six commonly-used modulation formats signals. A uniform grid is constructed in the first quadrant of two-dimensional (2D) Stokes plane to capture the received symbols sequence, and then the corresponding trajectory information is converted into the adjacent matrix via graph theory. The eigenvector associated with the largest eigenvalue of the adjacent matrix is selected as the discriminated-feature of the corresponding modulation format signal. Subsequently, we employ cosine similarity algorithm to obtain the modulation format of the unknown signal by analyzing the angle between the discriminated-features of the canonical modulation formats signals and the unknown signal. Then, the effectiveness of the proposed MFI scheme is verified through 28 GBaud polarization division multiplexing (PDM)-binary phase shift keying (BPSK), PDM-quadrature phase shift keying (QPSK), PDM-8 quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM, and PDM-64QAM simulation systems. The simulation results show that the proposed MFI scheme achieves well performance on the required minimum optical signal-to-noise ratio (OSNR) value, the robustness to the variation of the transmission environment, residual chromatic dispersion (CD) and different group delay (DGD). Finally, the proposed MFI scheme is further verified by 20 GBaud PDM-QPSK/8QAM/-16QAM/-32QAM long-haul transmission experiments, and the results demonstrate that the proposed MFI scheme exhibits good resilience towards fiber nonlinear impairments.

Modulation format identification based on phase statistics in Stokes space

In this work, a high-performance, low-complexity modulation format identification (MFI) technique is proposed and experimentally demonstrated for coherent optical receivers using features extracted from phase statistics in Stokes space. Numerical simulations as well as long-haul experiments are carried out to validate the proposed MFI scheme. Successful identification is demonstrated among polarization-multiplexed (PM) QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM signals. Compared with existing modulation identification schemes, the proposed MFI method has an excellent recognition performance with a low computational complexity.

A modified PSO assisted blind modulation format identification scheme for elastic optical networks

By using a modified particle swarm optimization (M-PSO) design, a novel and blind modulation format identification (MFI) scheme was proposed for elastic optical networks (EONs) in this study. Compared with the traditional PSO algorithm, the sight of each particle in our M-PSO algorithm was strictly restricted to perform search of local extremum points (LEPs) on density distribution histograms (DDHs). In the experiment, the distributions of LEPs on DDHs depended on signals’ modulation formats, and then were regarded as the key identification metric. The proposed MFI scheme did not need prior information of optical signal noise rate (OSNR) in the identification processes, and showed a natural resistance to frequency offset and line width. The feasibility of the proposed scheme was verified by 28 GBaud polarization division multiplexing (PDM)-BPSK/QPSK/8QAM/16QAM transmission system, where the simulation results showed that the lowest OSNRs required to achieve 100% identification rate were all lower than their corresponding 7% forward error correction (FEC) thresholds (BER=3.8e−3). Furthermore, the tolerance of the residual chromatic dispersion (CD), differential group delay (DGD) and nonlinear effect were also verified under the simulation conditions. The study results manifest that the proposed MFI scheme showed interesting robustness to both linear and nonlinear impairments. Moreover, we also demonstrated a relatively simpler calculation complexity under the proposed MFI scheme.

A CNN-based cost-effective modulation format identification scheme by low-bandwidth direct detecting and low rate sampling for elastic optical networks

Modulation format identification (MFI) is an indispensable technique in future optical performance monitoring (OPM) for elastic optical networks. The existing MFI scheme based on coherent detection combined with digital signal processing technology has good chromatic dispersion (CD) tolerance, but the high cost prevents its practical application at numerous intermediate nodes. By contrast, the existing direct detection-based MFI has the advantage of low cost but the CD tolerance is poor. In this paper a low-cost direct detection-based MFI scheme is proposed. The scheme not only reduces the cost of the MFI module by low-bandwidth direct detection and low rate sampling, but also ensures high MFI accuracy by extracting and learning features of asynchronous amplitude histograms (AAH) through convolutional neural network (CNN) as it has better accuracy and stability of identification compared with artificial neural network (ANN) and deep neural network (DNN) with the same computational complexity. A maximum correlation coefficient of AAH sequences of different modulation formats is proposed as a metric of discrimination for MFI, and its relationship with the bandwidth of MFI module is investigated in the scenario that the MFI module relative bandwidth changes from 0.00025 to 0.75 of service signal baud rate (SBR) and the CD vary from 0∼16000ps/nm. The simulation and experimental results show that the MFI module with 0.002∼0.02 SBR bandwidth could realize 100% MFI accuracy of three common QAM signals with the CD of 0∼16000ps/nm.

Blind optical modulation format identification assisted by signal intensity fluctuation for autonomous digital coherent receivers

A novel and blind optical modulation format identification (MFI) scheme assisted by signal intensity fluctuation features is proposed for autonomous digital coherent receivers of next generation optical network. The proposed MFI scheme needn't to pre-know OSNR value of incoming signal, even though it is well known that the intensity dependent features of the incoming signal changes as the change of OSNR performance. Here, the proposed scheme firstly utilizes two kinds of signal intensity fluctuation features, Godard's criterion error and intensity noise variance, to construct a two-dimensional (2D) plane where three different regions which consist of QPSK region, 8QAM region, mixed 16/32/64QAM region can be found. Thus, we can firstly identify QPSK and 8QAM from the 2D plane, and then partition Godard's criterion error method is introduced to further identify 16QAM, 32QAM and 64QAM in our proposed scheme. The performance of the proposed scheme is firstly verified by a series of numerical simulations in 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM coherent optical communication systems. The results show that the lowest required OSNR values to achieve 100% recognition rate for all modulation format signals are even lower than or close to their corresponding theoretical 20% FEC limits (BER = 2.4 × 10-2). Finally, the feasibility is further demonstrated via a series of proof-of-concept experiments among 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM systems under back-to-back and long-haul fiber transmission links (from 320 km to 2000km). Experiment results show that the proposed scheme is robust to both linear and nonlinear noise.

Joint and Accurate OSNR Estimation and Modulation Format Identification Scheme Using the Feature-Based ANN

A joint and accurate optical signal-to-noise ratio (OSNR) estimation and modulation formats identification (MFI) scheme based on the artificial neural network (ANN) is proposed and demonstrated via both simulation and the experiment system. The proposed scheme employs ANN to estimate OSNR and modulation formats from the OSNR and modulation formats dependent features, kurtosis, and amplitude variance. Simulation results show that the proposed scheme can achieve high OSNR estimation and MFI accuracy over wide OSNR ranges for the commonly used modulation formats such as QPSK, 8 quadratic-amplitude modulation (QAM), 16QAM, and 64QAM. Meanwhile, experimental results also indicate that the mean OSNR estimation errors are 0.15 dB, 0.41 dB, and 0.49 dB for QPSK, 8QAM, and 16QAM over wide ranges OSNR of 10–17 dB, 14–20 dB, and 17–25 dB, respectively. Additionally, 100% MFI accuracy for the commonly used modulation formats in our scheme is also confirmed experimentally. Compared with the convolutional neural network and the deep neural network, the proposed scheme shows comparable estimation and identification performance, and needs less computational resource. Therefore, our scheme can be considered an attractive technique for joint OSNR estimation and MFI in future reconfigurable optical networks.

Amplitude variance and 4th power transformation based modulation format identification for digital coherent receiver

This paper demonstrates the use of amplitude variance in combination with only one time of 4th power transformation and fast Fourier transform (FFT) for modulation format identification (MFI) in digital coherent receiver. The incoming signals are firstly classified into two main categories, polarization-division multiplexing (PDM) m-ary phase-shift keying modulation (mPSK) and m-ary quadrature amplitude modulation (mQAM), based on the amplitude variance after constant modulus algorithm (CMA) equalization. Then, the sub-categories of PDM-mPSK or PDM-mQAM are further identified by utilizing the logical regression (LR) algorithm on a two-dimensional (2-D) plane which is constructed by using the mean and maximum value after 4th power transformation and FFT. The feasibility is firstly verified via numerical simulation for 28GBaud/s PDM-QPSK/8PSK/16QAM/32QAM signals transmitted through additive Gaussian noise channel as well as long-distance standard single mode fiber (SSMF) channel. The simulation results demonstrate that 100% identification accuracy for all these modulation formats can be achieved at the optical signal noise ratio (OSNR) much lower than their respective 7% FEC thresholds and as high as 99% identification accuracy can be achieved even at the OSNR much lower or close to their respective 20% FEC thresholds. 100% identification accuracy can be also obtained in the presence of obviously nonlinear impairments. Proof-of-concept experiments are finally implemented to evaluate the MFI performance among 28GBaud/s PDM-QPSK/8PSK/16QAM, and 21.5GBaud/s PDM-32QAM signals, which further confirm the feasibility of our proposed MFI scheme.

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

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-to-average 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 ±π4,±3π4 (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-to-back (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 BER=2×10-2 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.

A Modulation Format Identification Method Based on Amplitude Deviation Analysis of Received Optical Communication Signal

Modulation format identification (MFI) is necessary for future elastic optical networks. We propose an MFI scheme based on the amplitude deviation analysis of the received optical communication signal. In our method, for a received optical signal, two values of normalized average amplitude deviation with reference to amplitude of ideal polarization-division multiplexing (PDM) QPSK and 16 quadratic-amplitude modulation (QAM) are computed, respectively, and the ratio of the two values is used to identify the commonly used formats like PDM-QPSK, PDM-8 QAM, PDM-16 QAM, PDM-32 QAM, and PDM-64 QAM. The results demonstrate that the modulation format can be identified with the required optical signal-to-noise ratio not higher than the forward error correction (FEC) threshold. The advantage of our method is that it is not sensitive to phase noise, frequency offset, and nonlinearity in optical fiber communication systems. The proposed method needs no training samples or additional hardware, so it is simple and cost-effective. Meanwhile, we also discuss the influence of the residual chromatic dispersion on our proposed scheme.

Blind modulation format identification using the DBSCAN algorithm for continuous-variable quantum key distribution

Advances in adaptive modulation techniques have fueled the growth of classic communication recently, and the modulation format identification (MFI) has been extensively studied in the field of wireless communication, but in order to make Alice and Bob smoothly enter post-processing and develop toward an adaptive network, the MFI concept is worth reviewing for a continuous-variable quantum key distribution (CV-QKD) system. This paper proposes a constellation MFI scheme based on the density-based spatial clustering of applications with noise (DBSCAN) machine learning algorithm, and the MFI process is set at the receiving end of the CV-QKD system. The proposed MFI scheme for 4-QAM, 16-QAM, 64-QAM, and 256-QAM signals can reach high accuracy (>99%) when the signal-to-noise ratio (SNR) is greater than or equal to 23 dB. The simulation results show that using the DBSCAN unsupervised machine learning algorithm can achieve good identification performance. The proposed MFI scheme can be further improved at lower SNR by optimizing the algorithm and increasing the number of samples.