1.张家界航空工业职业技术学院 航空电气工程系,湖南 张家界 427000
2.湖南大学 电气与信息工程学院,湖南 长沙 410082
Received:17 June 2025,
Revised:2025-09-15,
Published:20 January 2026
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宋锐,邓春丽,李志良等.基于对偶学习的高效可见光通信非线性均衡器[J].光学技术,2026,52(01):68-74.
SONG Rui,DENG Chunli,LI Zhiliang,et al.Efficient nonllinear equalizer for visible light communication based on dual learning[J].OPTICAL TECHNIQUE,2026,52(01):68-74.
针对室内可见光通信(VLC)中由LED动态非线性和信道衰落所引起的信号失真问题,提出一种基于对偶学习的非线性均衡方法。通过搭建PAM4调制VLC仿真系统,结合LED非线性模型与NLOS信道以模拟失真特性,设计了基于对偶神经网络结构的均衡器,利用正向均衡与反向映射网络实现双向约束,并借助对偶一致性损失提升在有限训练样本下的均衡性能。实验结果表明,方法显著降低了系统误码率,在相同训练符号数量下,误码率较传统均衡方法最多可降低2个数量级;与传统神经网络均衡器相比,达到FEC阈值所需训练序列长度最大可减少62.5%,能够在显著提升VLC系统传输可靠性的同时,大幅降低均衡器训练开销。
To address signal distortion caused by LED dynamic nonlinearity and channel fading in indoor visible light communication (VLC) systems
a nonlinear equalization method based on dual learning is proposed. A PAM4-modulated VLC simulation system is constructed
incorporating an LED nonlinearity model and a non-line-of-sight (NLOS) channel to emulate distortion characteristics. A dual neural network structure is designed
which employs a forward equalization network and a backward mapping network to achieve bidirectional constraints
and utilizes a dual consistency loss to enhance equalization performance under limited training samples. Experimental results show that the proposed method significantly improves the bit error rate (BER) performance of the system. Under the same number of training symbols
the BER can be reduced by up to two orders of magnitude compared to conventional equalization methods. Compared to traditional neural network equalizers
the required training sequence length to achieve the FEC threshold is reduced by up to 62.5%
effectively enhancing the transmission reliability of the VLC system while substantially reducing the training overhead.
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