Zhang Yu, Zhou Peng, Liu Yan, Wang Jixiang, Li Chuanqi, Lu Ye
Opt Express. 2023 Jul 3;31(14):23183-23197. doi: 10.1364/OE.488829.
An algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target. Ablation experiments including multi-task model-agnostic meta-learning (MT-MAML), single-task model-agnostic meta-learning (ST-MAML) and adaptive multi-task learning (AMTL) are executed to train a data set with only 20 examples for each class. First, we discuss the impact from the number of shots and gradient descent steps for support set on the meta-learning based schemes to determine the best hyper parameters and conclude that the proposed method better captures the similarity between new and previous knowledge at 4 shot and 1 step. Withdrawn fine-tuning, the model achieves the lowest error ∼0.37 dB initially. Then, we simulate two other schemes (AMTL and ST-MAML), and the numerical results shows that mean square error (MSE) are ∼0.6 dB, ∼0.3 dB and ∼0.18 dB, respectively, proposed method has faster adaption to main task. For low order modulation formats, the proposed method almost reduces the error to 0. Meanwhile, we reveal the degree of deviation between the prediction and target and find that the deviation is mainly concentrated in the high OSNR range of 25-40 dB. Specifically, we investigate the variation curve of adaptive weights during pretraining and conclude that after 30 epoch, the model's attention was almost entirely focused on estimating OSNR. In addition, we study the generalization ability of the model by varying the transmission distance. Importantly, excellent generalization is also experimentally verified. In this paper, the method proposed will greatly reduce the cost for repetitively collecting data and the training resources required for fine-tuning models when OPM devices need to be deployed at massive nodes in dynamic optical networks.
提出了一种用于少样本学习（FSL）联合调制格式识别（MFI）和光信噪比（OSNR）估计的算法。通过32 GBaud的双偏振（DP）相干检测系统，在10 - 40 dB的宽OSNR范围内获得了六种广泛使用的调制格式的星座图。我们将辅助任务引入到模型无关元学习（MAML）中，这使得元任务的梯度在最优目标方向上下降得更快。执行了消融实验，包括多任务模型无关元学习（MT - MAML）、单任务模型无关元学习（ST - MAML）和自适应多任务学习（AMTL），以训练每个类别仅有20个示例的数据集。首先，我们讨论了支持集的样本数量和梯度下降步数对基于元学习的方案的影响，以确定最佳超参数，并得出所提出的方法在4个样本和1步时能更好地捕捉新知识与旧知识之间的相似性的结论。在不进行微调的情况下，该模型最初实现了约0.37 dB的最低误差。然后，我们模拟了另外两种方案（AMTL和ST - MAML），数值结果表明均方误差（MSE）分别约为0.6 dB、0.3 dB和0.18 dB，所提出的方法对主要任务具有更快的适应性。对于低阶调制格式，所提出的方法几乎将误差降低到0。同时，我们揭示了预测值与目标值之间的偏差程度，并发现该偏差主要集中在25 - 40 dB的高OSNR范围内。具体而言，我们研究了预训练期间自适应权重的变化曲线，并得出在30个轮次之后，模型的注意力几乎完全集中在估计OSNR上的结论。此外，我们通过改变传输距离来研究模型的泛化能力。重要的是，出色的泛化能力也通过实验得到了验证。在本文中，当需要在动态光网络中的大量节点上部署光性能监测（OPM）设备时，所提出的方法将大大降低重复收集数据的成本以及微调模型所需的训练资源。
