Generalized Machine Learning Equalization in Coherent Receivers

Dataset underpinning manuscript "Generalized Machine Learning Equalization in Coherent Receivers". <br> "graph_data.xlsx" is an excel spreadsheet containing the graph data. There are two sheets, "Launch Power" which contains the data in Fig 2a, and "Dispersion" containing the data in Fig 2b. In each sheet, the first column is the X axis and further columns are the Y values. <br> "train_data.pt" and "val_data.pt" contain the training and validation data used in this paper (testing data was generated at test time). Both were generated in Pytorch 2.1, and can be accessed using the built in "torch.load" function supplied by Pytorch. Each file contains a list of dictionaries with keys "rx_training_sequece", "rx_data_sequence", and "tx_data_symbols", denoting the received training sequence, received data sequence, and transmitted symbols respectively, with the corresponding values being complex tensors. <br> "training_sequence.pt" contains the pulse-shaped training sequence, generated in Pytorch 2.1 and can be accessed in the same way as above, containing a complex tensor.

支撑学术论文《相干接收机中的广义机器学习均衡（Generalized Machine Learning Equalization in Coherent Receivers）》的配套数据集。

"graph_data.xlsx"为存储绘图数据的电子表格文件，内含两个工作表："Launch Power"工作表存储图2a对应的数据，"Dispersion"工作表存储图2b对应的数据。每个工作表的第一列为X轴数据，其余列均为Y轴数据。

"train_data.pt"与"val_data.pt"分别存储本文所用的训练数据集与验证数据集（测试数据集于测试阶段实时生成）。二者均基于PyTorch 2.1生成，可通过PyTorch内置的`torch.load`函数加载。每个文件均为字典列表，字典包含三个键："rx_training_sequece"、"rx_data_sequence"与"tx_data_symbols"，分别对应接收训练序列、接收数据序列与发射符号，其对应值均为复数值张量。

"training_sequence.pt"存储经过脉冲成型的训练序列，同样基于PyTorch 2.1生成，加载方式与前述文件一致，其内部数据为复数值张量。