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

Yu, Xinkuo; Chen, Bai; Xu, Hengying; Sun, Weibin; Yang, Lishan; Zhang, Hongjun; Hu, Weisheng

doi:10.1016/j.optcom.2020.126280

Cited by 16 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e PSO algorithm is a classical population optimization algorithm proposed in 1995, which is fast in iterations and has better global than local optimization seeking ability [20]. e particle swarm position update equation is as follows:…”

Section: Particle Swarm Algorithmmentioning

confidence: 99%

“…The PSO algorithm is a classical population optimization algorithm proposed in 1995, which is fast in iterations and has better global than local optimization seeking ability [ 20 ]. The particle swarm position update equation is as follows:

where w is the weight of V id k , and P p and P g are the individual optimum and group optimum, respectively.…”

Section: Segmentation Of Retinal Vessels Based On Pmssa Multi-thresho...mentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images

Deng

Liu

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper’s improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort.

show abstract

Section: Particle Swarm Algorithmmentioning

confidence: 99%

where w is the weight of V id k , and P p and P g are the individual optimum and group optimum, respectively.…”

Section: Segmentation Of Retinal Vessels Based On Pmssa Multi-thresho...mentioning

confidence: 99%

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images

Deng

Liu

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…To further evaluate the performance of the proposed MFI scheme, the non-iterative clustering algorithm in [28], the density-peak-based method in [29], the DNN-based method in [? ], the amplitude-distribution-based method in [30] and the modified-PSO-based method in [24] are used to compare the minimum required OSNR and the number of symbols for 100% perfect identification, which is shown in Fig. 13.…”

Section: The Comparison With the Required Symbols And Minimal Osnr Valuementioning

confidence: 99%

“…method [24], principal component analysis and singular value decomposition algorithm [25]. For the review of MFI researches above, compared with the decision-parameter based type and aided information based type, machine learning based algorithms have attracted significant attentions with the advantage of flexible and high-performance.…”

mentioning

confidence: 99%

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

Yang

Chen

et al. 2021

IEEE Photonics J.

Self Cite

View full text Add to dashboard Cite

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), PDMquadrature 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-tonoise 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.

show abstract

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 16 publications

References 24 publications

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images

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

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

Contact Info

Product

Resources

About