ESN-Driven joint decision strategy for a configurable security NOMA optical communication system

This paper presents an Echo State Network (ESN)-Driven joint decision strategy for a configurable security Non-Orthogonal Multiple Access (NOMA) optical communication system. To address security vulnerabilities, a configurable key iteration scheme is introduced, achieving temporal interleaving of chaotic models and keys. Furthermore, an ESN-Driven joint demodulation strategy is designed, which replaces conventional log-likelihood ratio (LLR) computations with ESN, leading to a reduction in computational overhead. Experimental validation over a 7-core fiber demonstrates that the ESN-Driven joint decision strategy maintains low computational cost while increasing Brute-Force Time (BFT) to 10<sup>151</sup>, thereby significantly enhancing efficiency and security of the system.

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Publication Details

Journal
Optics Letters
Published
2025-11-26
DOI
https://doi.org/10.1364/ol.583147
Citations
3
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
7.44
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ESN-Driven joint decision strategy for a configurable security NOMA optical communication system

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Optics Letters
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article

ESN-Driven joint decision strategy for a configurable security NOMA optical communication system

Jianxin Ren, xiumin song, Yaya Mao, Yiming Ma, Shuaidong Chen, Yilan Ma, Lei Zhu, Yu Bai, Jianye Zhao
article en
3 citations

Abstract

This paper presents an Echo State Network (ESN)-Driven joint decision strategy for a configurable security Non-Orthogonal Multiple Access (NOMA) optical communication system. To address security vulnerabilities, a configurable key iteration scheme is introduced, achieving temporal interleaving of chaotic models and keys. Furthermore, an ESN-Driven joint demodulation strategy is designed, which replaces conventional log-likelihood ratio (LLR) computations with ESN, leading to a reduction in computational overhead. Experimental validation over a 7-core fiber demonstrates that the ESN-Driven joint decision strategy maintains low computational cost while increasing Brute-Force Time (BFT) to 10<sup>151</sup>, thereby significantly enhancing efficiency and security of the system.

Optics LettersVol. 51(1)
Suzhou Polytechnic Institute of Agriculture (CN), Nanjing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 3%
Neural Networks and Reservoir Computing
7.44
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