Data supporting “Gaussian Processes based Optimized Launch Power for Nonlinear Optical Fiber Transmission Links”

Repository of raw data files and Matlab scripts required to reproduce results presented in “Gaussian Processes based Optimized Launch Power for Nonlinear Optical Fiber Transmission Links” %%%%%%%%%%%%%%%%%%%%%%%%%%%%% Each dataset contains the received constellation signal to noise ratio as a function of the launch optical power into each span. Experiment: 10 spans, 100km of SSMF with launch VOA and post span EDFA. Signal 31.7 GBd PM-QPSK private signal modulated by Ciena WaveLogic3 and received by a 25 GHz ICR , 100GSa DSO, and private DSP. The data file in Matlab format 7.3, contains a single structure with: Results.Description - some notes Results.LP - launch powers into each span, and receiver [dBm] Results.mleSNR - constellation SNR [dB] Results.base_LP - prior knowledge or start point or sweep centre of optimum [dBm] %%%%%%%%%%%%%%%%%%%%%%%%%%%%% SingleChannelRandom_170919_R1_Public.mat Analyze with either GP_optimise_hypopt.m, GQ_optimise.m or PT_optimise.m There are three Matlab scripts to find the optimum launch power based on: GP - a Gaussian Process, GQ - a generalised Quadratic and PT - a physical transmission model. They have options for number of samples, GP_optimise_hypopt has options to optimise hyper-parameters and to include hyper-prior training. Used to produce figures 2,3,5 and 6 %%%%%%%%%%%%%%%%%%%%%%%%%%%%% SingleChannelScan_200820_R4_Public.mat Analyze with PowerSweepCalc.m Used to produce figures 4 SingleChannelScan_290720_R2_Public.mat used to check PT result %%%%%%%%%%%%%%%%%%%%%%%%%%%%% SingleChannelBayesian_140720_R1_Public.m SingleChannelBayesian_140720_R2_Public.m SingleChannelBayesian_140720_R3_Public.m SingleChannelBayesian_140720_R5_Public.m SingleChannelBayesian_140720_R6_Public.m 5 repeat runs Analyze with PlotBayesianResults.m Used to produce figures 7 and 8 %%%%%%%%%%%%%%%%%%%%%%%%%%%%% EmulatedSingleChannelBayesian_140720_Public.m BayesOpt.m MeasureSNR.m provide an emulation of the Bayesian aquisition based optimisation. The experimental measurement of SNR is replaced by an emulation based on the PT model and provided by the MeasureSNR.m function. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% This work was supported by the UK Engineering and Physical Sciences Research Council through programme grant TRANSNET EP/R035342/1 and Ciena. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%

本仓库包含复现《基于高斯过程的非线性光纤传输链路最优发射功率》一文结果所需的原始数据文件与Matlab脚本。
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每个数据集均记录了接收星座图信噪比随各跨段发射光功率变化的关系数据。
实验配置：10个跨段、每段100km的标准单模光纤（Standard Single-Mode Fiber, SSMF），链路部署有发射端可变光衰减器（Variable Optical Attenuator, VOA）以及跨段后掺铒光纤放大器（Erbium-Doped Fiber Amplifier, EDFA）。
传输信号为31.7 GBd的偏振复用正交相移键控（Polarization Multiplexed Quadrature Phase Shift Keying, PM-QPSK）私有信号，由Ciena WaveLogic3芯片调制，接收系统包含25 GHz干涉式相干接收机（Interferometric Coherent Receiver, ICR）、100GSa数字存储示波器（Digital Sampling Oscilloscope, DSO）以及私有数字信号处理器（Digital Signal Processor, DSP）。
该数据文件采用Matlab 7.3格式存储，包含单个结构体，各字段说明如下：
Results.Description：备注信息
Results.LP：各跨段发射功率与接收端功率（单位：dBm）
Results.mleSNR：星座图信噪比（单位：dB）
Results.base_LP：最优发射功率的先验知识、初始扫描点或扫描中心（单位：dBm）
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`SingleChannelRandom_170919_R1_Public.mat`
可通过`GP_optimise_hypopt.m`、`GQ_optimise.m`或`PT_optimise.m`进行分析。
上述三个Matlab脚本可基于不同方法求解最优发射功率：
- GP：基于高斯过程（Gaussian Process）
- GQ：基于广义二次（Generalised Quadratic）方法
- PT：基于物理传输模型（Physical Transmission Model）
其中`GP_optimise_hypopt.m`支持配置采样点数，还可实现超参数优化与超先验训练功能。
本数据集用于生成论文图2、图3、图5及图6。
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`SingleChannelScan_200820_R4_Public.mat`
可通过`PowerSweepCalc.m`进行分析，用于生成论文图4。
`SingleChannelScan_290720_R2_Public.mat`用于验证PT模型的计算结果。
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以下为5次重复运行的脚本文件：
`SingleChannelBayesian_140720_R1_Public.m`
`SingleChannelBayesian_140720_R2_Public.m`
`SingleChannelBayesian_140720_R3_Public.m`
`SingleChannelBayesian_140720_R5_Public.m`
`SingleChannelBayesian_140720_R6_Public.m`
可通过`PlotBayesianResults.m`进行分析，用于生成论文图7与图8。
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`EmulatedSingleChannelBayesian_140720_Public.m`、`BayesOpt.m`与`MeasureSNR.m`
实现了基于贝叶斯采集的优化仿真流程：将信噪比的实验测量值替换为基于PT模型的仿真结果，该仿真功能由`MeasureSNR.m`函数提供支持。
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本研究得到英国工程与自然科学研究委员会（Engineering and Physical Sciences Research Council, EPSRC）通过TRANSNET项目（项目编号EP/R035342/1）以及Ciena公司的资助。