University College London
4 files

Generalized Machine Learning Equalization in Coherent Receivers

posted on 2023-10-27, 09:54 authored by Sam LennardSam Lennard, Filipe Marques FerreiraFilipe Marques Ferreira

Dataset underpinning manuscript "Generalized Machine Learning Equalization in Coherent Receivers".

"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.

"" and "" 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.

"" 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.


Beyond Exabit Optical Communications: from new devices, via new dimensions to new systems

UK Research and Innovation

Find out more...


Usage metrics

    Department of Electronic & Electrical Engineering