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Performance analysis of geometrically shaped 16/32/64/128QAM based on swarm intelligence algorithm.

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Title: Performance analysis of geometrically shaped 16/32/64/128QAM based on swarm intelligence algorithm.
Authors: Lu, Jia1,2,3 (AUTHOR), Wang, Tianshuo1,2,3 (AUTHOR), Ma, Jie1,2,3 (AUTHOR) jie.ma@hebut.edu.cn, Liu, Jianfei1,2,3 (AUTHOR), Zeng, Xiangye1,2,3 (AUTHOR), Wang, Yang1,2,3 (AUTHOR)
Source: Optical Fiber Technology. Mar2025, Vol. 90, pN.PAG-N.PAG. 1p.
Subjects: Optimization algorithms, Quadrature amplitude modulation, Swarm intelligence, Optical communications, Phase noise, Particle swarm optimization
Abstract: • We propose geometric shaping based on swarm intelligence algorithms. • From 16QAM to 128QAM, the advantages of this solution increase even more obvious. • Add randomness to avoid the algorithm falling into the local optimum. • Enhance tolerance to nonlinearity for high-order signals with the scheme. • OSNR gain enhancement of 1.4/1.6/2.8/4.3 dB using swarm intelligence algorithm. In this paper, an optimization scheme for geometrically shaped quadrature amplitude modulation (GS-QAM) based on swarm intelligence algorithms is proposed. The swarm intelligence algorithms of the marine predator algorithm (MPA), nonlinear marine predator algorithm (NMPA), mountain gazelle optimizer (MGO), dog optimization algorithm (DOA), and honey badger algorithm (HBA) are used to optimize the geometrical locations of the constellation points to reduce the damage caused by phase noise and improve the system performance. The results show that the scheme improves the optical signal-to-noise ratio (OSNR) gain by 1.4 dB/1.6 dB/2.8 dB/4.3 dB, compared with the standard 16/32/64/128 QAM signals and the optimization effect becomes more obvious as the modulation order increases. The five algorithms also significantly improve the performance of the system in terms of transmission distance and transmission rate. In addition, the scheme further validates the universality of the proposed optimization scheme for different modulation formats and demonstrates its potential application to higher-order signals. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• We propose geometric shaping based on swarm intelligence algorithms. • From 16QAM to 128QAM, the advantages of this solution increase even more obvious. • Add randomness to avoid the algorithm falling into the local optimum. • Enhance tolerance to nonlinearity for high-order signals with the scheme. • OSNR gain enhancement of 1.4/1.6/2.8/4.3 dB using swarm intelligence algorithm. In this paper, an optimization scheme for geometrically shaped quadrature amplitude modulation (GS-QAM) based on swarm intelligence algorithms is proposed. The swarm intelligence algorithms of the marine predator algorithm (MPA), nonlinear marine predator algorithm (NMPA), mountain gazelle optimizer (MGO), dog optimization algorithm (DOA), and honey badger algorithm (HBA) are used to optimize the geometrical locations of the constellation points to reduce the damage caused by phase noise and improve the system performance. The results show that the scheme improves the optical signal-to-noise ratio (OSNR) gain by 1.4 dB/1.6 dB/2.8 dB/4.3 dB, compared with the standard 16/32/64/128 QAM signals and the optimization effect becomes more obvious as the modulation order increases. The five algorithms also significantly improve the performance of the system in terms of transmission distance and transmission rate. In addition, the scheme further validates the universality of the proposed optimization scheme for different modulation formats and demonstrates its potential application to higher-order signals. [ABSTRACT FROM AUTHOR]
ISSN:10685200
DOI:10.1016/j.yofte.2024.104111