Perturbation dimensional reduction theory assisted low complexity convolutional neural network equalizer for optical communications

The integration of neural networks with perturbation theory has been recognized as a pivotal factor contributing to the enhancement of transmission efficiency in modern optical communication systems. Research focuses on balancing superior equalization performance against system complexity minimization. This paper presents a novel and efficient data dimensionality reduction compression methodology for developing low-complexity neural network-based equalizers intended to mitigate nonlinear impairments in optical fiber communication systems. Firstly, we propose a dimensionality reduction (DR) method exploiting the spatial symmetry of perturbation coefficients, enabling low-complexity DR feature map construction at the input end. Then, a complex-valued CNN (CvCNN) equalizer is then designed, utilizing single-channel complex-valued feature maps to preserve data characteristics while further reducing complexity. The effectiveness of the proposed equalizer is verified through numerical studies, and further performance comparisons and complexity analyses with other equalizers are conducted in a 20 GBaud PDM 64-QAM coherent optical transmission experimental system. The research and analysis are grounded in a fair performance comparison of Q-factors, accompanied by thorough analyses of time and space complexity under equivalent performance conditions. Finally, The DR feature map achieves an 82.64% reduction in effective feature units. When deployed in CvCNN, the equalizer reduces space and time complexity by 66.90% and 70.55%, respectively.