├── .gitignore ├── .gitattributes ├── Script_ResampleSignal.m ├── Script_TxDataGeneration.m ├── Script_TxDataRetrieval.m ├── LICENSE.txt ├── Script_DataDelayTest.m ├── Script_Ex_LinearFFEqualization2.m ├── syncAndExtractSignal.m ├── Script_Ex_MLSEEqualization.m ├── Script_Ex_LinearFFEqualization.m ├── Script_50G_FFE_201802.m ├── Script_SequenceExtraction.m ├── decisionAndCalcBerPAM4.m ├── Script_playground.m ├── Script_50G_VolterraSOA.m ├── mlseEqualize.m ├── lowPassFilter25G.m ├── Script_50G_NoSOA.m ├── generateData.m ├── README.md ├── linearFFEqualize.m ├── Script_50G_SequenceExtraction.m ├── Script_RoF_Wireless_SquareLawDetection.m ├── Script_RoF_Wireless_CoherentDetection.m ├── Script_RoFWired.m ├── Script_PTLSim.m ├── volterraFFEqualize.m ├── bandPassFilter15G35G.m ├── bandPassFilter12G38G.m └── lowPassFilter12_5G.m /.gitignore: -------------------------------------------------------------------------------- 1 | Original Data/* 2 | Sampled Data/* 3 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /Script_ResampleSignal.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 28e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | FileDir = '.\Sampled Data\201710\28G_PAM4\obtb\'; 13 | files = dir([FileDir, 'extracted\']); 14 | 15 | for i = 1 : length(files) - 2 16 | SampledSignal = importdata([FileDir, 'extracted\', files(i).name]); 17 | ExtractedSignal = SampledSignal(1 : OverSamplingRatio : end); 18 | csvwrite([FileDir, 'extracted_1sps\', files(i).name], ExtractedSignal); 19 | end 20 | 21 | load splat 22 | sound(y,Fs) 23 | -------------------------------------------------------------------------------- /Script_TxDataGeneration.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | DataLength = 500000; 9 | NumOfSeq = 5; 10 | rng('shuffle'); 11 | PAM4Data = randi(4, DataLength, 1) - 1; 12 | PAM4Data = reshape(PAM4Data, DataLength / NumOfSeq, NumOfSeq); 13 | PAM8Data = randi(8, DataLength, 1) - 1; 14 | PAM8Data = reshape(PAM8Data, DataLength / NumOfSeq, NumOfSeq); 15 | PAM16Data = randi(16, DataLength, 1) - 1; 16 | PAM16Data = reshape(PAM16Data, DataLength / NumOfSeq, NumOfSeq); 17 | 18 | if ~exist('Original Data', 'dir') 19 | mkdir('Original Data'); 20 | end 21 | 22 | for i = 1 : NumOfSeq 23 | for j = [4, 8, 16] 24 | csvwrite(['Original Data\pam', num2str(j), '_', num2str(i), '.csv'], eval(['PAM', num2str(j), 'Data(:, ', num2str(i), ')'])); 25 | end 26 | end 27 | 28 | PAM4Data = PAM4Data / 3; 29 | PAM8Data = PAM8Data / 7; 30 | PAM16Data = PAM16Data / 15; 31 | -------------------------------------------------------------------------------- /Script_TxDataRetrieval.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | if ~exist('Original Data', 'dir') 9 | error('Original Data fold does not exist'); 10 | end 11 | 12 | DataLength = 500000; 13 | NumOfSeq = 5; 14 | PAM4Data = zeros(DataLength / NumOfSeq, NumOfSeq); 15 | PAM8Data = zeros(DataLength / NumOfSeq, NumOfSeq); 16 | PAM16Data = zeros(DataLength / NumOfSeq, NumOfSeq); 17 | 18 | for i = 1 : NumOfSeq 19 | for j = [4, 8, 16] 20 | switch j 21 | case 4 22 | PAM4Data(:, i) = importdata(['.\Original Data\pam4_', num2str(i), '.csv']); 23 | case 8 24 | PAM8Data(:, i) = importdata(['.\Original Data\pam8_', num2str(i), '.csv']); 25 | case 16 26 | PAM16Data(:, i) = importdata(['.\Original Data\pam16_', num2str(i), '.csv']); 27 | end 28 | end 29 | end 30 | 31 | PAM4Data = PAM4Data / 3; 32 | PAM8Data = PAM8Data / 7; 33 | PAM16Data = PAM16Data / 15; 34 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) [2017] [Peixuan Li] 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /Script_DataDelayTest.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | OSCRate = 80e9; 6 | DataRate = 28e9; 7 | SampleRate = lcm(OSCRate, DataRate); 8 | OverSamplingRatio = SampleRate / DataRate; 9 | 10 | SampledSignal = importdata('.\Sampled Data\50G PAM4\201708\cal\ebtb\C2ebtb00000.dat'); 11 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 12 | 13 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 14 | 15 | ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(SampledSignal), max(SampledSignal)]); 16 | step(ed, SampledSignal); 17 | 18 | tic 19 | OriginalSignal = importdata('.\Original Data\prbs15pam4.mat'); 20 | OriginalSignal = OriginalSignal'; 21 | OriginalSignal = (OriginalSignal - mean(OriginalSignal)) * 6; 22 | 23 | OriginalData_port1 = OriginalSignal; 24 | shiftnum = 61; 25 | % OriginalData_port2 = [~(OriginalSignal(shiftnum + 1 : end)); ... 26 | % ~(OriginalSignal(1 : shiftnum))]; 27 | OriginalData_port2 = [~(OriginalSignal(end - shiftnum + 1 : end)); 28 | ~(OriginalSignal(1 : end - shiftnum))]; 29 | % OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 30 | 31 | OriginalData = OriginalSignal; 32 | CorrelationResult = zeros(length(SampledSignal) - OverSamplingRatio * length(OriginalData) + 1, 1); 33 | parfor i = 1 : length(CorrelationResult) 34 | CorrelationResult(i) = sum(SampledSignal(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 35 | end 36 | plot(CorrelationResult) 37 | toc 38 | -------------------------------------------------------------------------------- /Script_Ex_LinearFFEqualization2.m: -------------------------------------------------------------------------------- 1 | %% This example shows how to equalize all the dat file in the same directory 2 | %% and then draw a ber plot for the dat files with 1 points a file. 3 | 4 | % TODO The ber plot is not done yet 5 | 6 | clear all; 7 | close all; 8 | clc; 9 | 10 | %% change the current directory to the folder which contains this m file 11 | cd(fileparts(which(mfilename))); 12 | 13 | %% Generate original data 14 | OriginalData = generateData(); 15 | 16 | %% Import sampled data from DSO 17 | % Defining parameters 18 | SampleRate = 400e9; 19 | DataRate = 12.5e9; 20 | OverSamplingRatio = SampleRate / DataRate; 21 | % importing and eyediagram drawing 22 | SampleDir = '.\Sampled Data\40km+FBG+FILTER+0\'; 23 | files = dir(SampleDir); 24 | for i = 1 : length(files) - 2 25 | SampledSignal = importdata(strcat(SampleDir,files(i + 2).name)); 26 | % eyediagram(SampledSignal(1:100000), 4*OverSamplingRatio, 2*OverSamplingRatio, 0.5*OverSamplingRatio); 27 | % grid on; 28 | 29 | %% Signal Synchronization and Extraction 30 | [ExtractedSignal(:,i), OriginalSignal(:,i)] = syncAndExtractSignal(SampledSignal, OriginalData, OverSamplingRatio); 31 | 32 | %% LMS Equalization 33 | % 101-tap FFE and training for 20 epochs 34 | [equalizedSignal(:,i), w] = linearFFEqualize(ExtractedSignal(:,i), OriginalSignal(:,i), 'lms', 101, 0.01, 20); 35 | % Equalization Result Visulization 36 | % eyediagram(equalizedSignal, 4*OverSamplingRatio, 2*OverSamplingRatio, 0.5*OverSamplingRatio); 37 | % grid on; 38 | 39 | %% Signal Decision and BER Calculation 40 | [BitErrorRate(i), SymErrorRate(i), BitErrorNum(i)] = decisionAndCalcBerPAM4(equalizedSignal(:,i), (OriginalSignal(:,i) + 3) / 2); 41 | fprintf('The %d th Bit eror numbers: %d \n', i, BitErrorNum(i)); 42 | fprintf('SER: %e\n', SymErrorRate(i)); 43 | fprintf('BER: %e\n\n', BitErrorRate(i)); 44 | end 45 | plot(log10(BitErrorRate)) -------------------------------------------------------------------------------- /syncAndExtractSignal.m: -------------------------------------------------------------------------------- 1 | function [ExtractedSignalUS, OriginalSignalUS] = syncAndExtractSignal(SampledData, OriginalData, OverSamplingRatio, UpSamplingRatio) 2 | % This function performs the synchronization and signal extraction for the 3 | % sampled signal from DSO. The %SampledData% will be down sampled by the 4 | % %OverSamplingRatio%/%UpSamplingRatio% and correlated with the %OriginalData% 5 | % upsampled by %UpSamplingRatio%. Then the index of max correlating coeffients 6 | % will be found and the synchronized data will be extracted. 7 | % 8 | % input: 9 | % SampledData 10 | % The data sampled by DSO. 11 | % OriginalData 12 | % The data which is transmitted with 1 symbol per second. 13 | % OverSamplingRatio 14 | % The ratio of down sampling progress, which is usually the DSO sample 15 | % rate dividing signal symbol rate 16 | % UpSamplingRatio (optional) 17 | % The ratio of up sampling progress. 18 | % Default value: 1 19 | % output: 20 | % ExtractedSignalUS 21 | % The synchronized and extracted signal upsampled by %UpSamplingRatio%. 22 | % Size: length(OriginalData)*UpSamplingRatio, 1 23 | % OriginalSignalUS 24 | % The original signal upsampled by %UpSamplingRatio%. 25 | % Size: length(OriginalData)*UpSamplingRatio, 1 26 | 27 | % Parameters checking 28 | narginchk(3,4); 29 | 30 | if ~exist('UpSamplingRatio','var') || isempty(UpSamplingRatio) 31 | UpSamplingRatio = 1; 32 | end 33 | 34 | % Downsampling 35 | % TODO Change the mechanism of sync; There are some bugs here 36 | DownSampledData = SampledData(1:OverSamplingRatio/UpSamplingRatio:end, 1); 37 | 38 | % Preparing transmitted data 39 | OriginalDataRemapped = (OriginalData - 1.5) * 2; 40 | OriginalSignalUS = reshape(repmat(OriginalDataRemapped, 1, UpSamplingRatio)', UpSamplingRatio * numel(OriginalDataRemapped), 1); 41 | 42 | % Correlation 43 | CorrelationResult = conv(DownSampledData(1:end), conj(OriginalSignalUS(end:-1:1))); 44 | [MaxCorr, index] = max(CorrelationResult); 45 | 46 | ExtractedSignalUS = DownSampledData(index-length(OriginalData)*UpSamplingRatio+1 : index); 47 | -------------------------------------------------------------------------------- /Script_Ex_MLSEEqualization.m: -------------------------------------------------------------------------------- 1 | %% This example shows the work flow for a receiver side dsp: data generation, 2 | %% synchronization, signal extraction, PAM4 decision and BER calculation. 3 | %% Also the eyediagram is drawn, but since the equalizer is 1 sample/sym, 4 | %% the equalized signal eyediagram cannot be drawn. What's more, the error 5 | %% performance of signal before and after equalization is compared. 6 | %% Equalization step uses MLSE equalizer. 7 | 8 | clear all; 9 | close all; 10 | clc; 11 | 12 | %% change the current directory to the folder which contains this m file 13 | cd(fileparts(which(mfilename))); 14 | 15 | %% Generate original data 16 | OriginalData = generateData(); 17 | 18 | %% Import sampled data from DSO 19 | % Defining parameters 20 | SampleRate = 400e9; 21 | DataRate = 12.5e9; 22 | OverSamplingRatio = SampleRate / DataRate; 23 | % importing and eyediagram drawing 24 | SampledSignal = importdata('.\Sampled Data\40km+FBG+FILTER+-680\F2_00011.dat'); 25 | eyediagram(SampledSignal(1:100000), 4*OverSamplingRatio, 2*OverSamplingRatio, 0.5*OverSamplingRatio); 26 | grid on; 27 | 28 | %% Signal Synchronization and Extraction 29 | [ExtractedSignal, OriginalSignal] = syncAndExtractSignal(SampledSignal, OriginalData, OverSamplingRatio); 30 | 31 | %% MLSE Equalization 32 | [EqualizedSignal, ChnlCoeffs, costs] = mlseEqualize(ExtractedSignal, OriginalSignal, 4); 33 | 34 | % plot the curve of convergence 35 | figure; 36 | plot(costs); 37 | title('Curve of Convergence'); 38 | xlabel('Epoch'); ylabel('Cost'); 39 | 40 | %% Signal Decision and BER Calculation 41 | % For the unequalized signal 42 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalSignal); 43 | fprintf('\nThe signal error before equalization\n'); 44 | fprintf('Bit number num: %d \n', BitErrorNum); 45 | fprintf('SER: %e\n', SymErrorRate); 46 | fprintf('BER: %e\n', BitErrorRate); 47 | 48 | % For the equalized signal 49 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalSignal); 50 | fprintf('\nThe signal error after equalization\n'); 51 | fprintf('Bit number num: %d \n', BitErrorNum); 52 | fprintf('SER: %e\n', SymErrorRate); 53 | fprintf('BER: %e\n', BitErrorRate); 54 | -------------------------------------------------------------------------------- /Script_Ex_LinearFFEqualization.m: -------------------------------------------------------------------------------- 1 | %% This example shows the work flow for a receiver side dsp: data generation, 2 | %% synchronization, signal extraction, PAM4 decision and BER calculation. 3 | %% Also the eyediagram is drawn, but since the equalizer is 1 sample/sym, 4 | %% the equalized signal eyediagram cannot be drawn. What's more, the error 5 | %% performance of signal before and after equalization is compared. 6 | 7 | clear all; 8 | close all; 9 | clc; 10 | 11 | %% change the current directory to the folder which contains this m file 12 | cd(fileparts(which(mfilename))); 13 | 14 | %% Generate original data 15 | OriginalData = generateData(); 16 | 17 | %% Import sampled data from DSO 18 | % Defining parameters 19 | SampleRate = 400e9; 20 | OSCRate = 80e9; 21 | DataRate = 25e9; 22 | OverSamplingRatio = SampleRate / DataRate; 23 | % importing and eyediagram drawing 24 | SampledSignal = importdata('.\Sampled Data\20170428\ebtb.txt'); 25 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 26 | eyediagram(SampledSignal(1:100000), 4*OverSamplingRatio, 2*OverSamplingRatio, 0.5*OverSamplingRatio); 27 | grid on; 28 | 29 | %% Signal Synchronization and Extraction 30 | [ExtractedSignal, OriginalSignal] = syncAndExtractSignal(SampledSignal, OriginalData, OverSamplingRatio); 31 | 32 | %% LMS Equalization 33 | % 101-tap FFE and training for 5 epochs 34 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalSignal, 'lms', 1001, 0.001, 5); 35 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalSignal, 'rls', 301, 0.99, 2); 36 | 37 | % plot the curve of convergence 38 | % figure; 39 | % plot(costs); 40 | % title('Curve of Convergence'); 41 | % xlabel('Epoch'); ylabel('Cost'); 42 | 43 | %% Signal Decision and BER Calculation 44 | % For the unequalized signal 45 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalSignal); 46 | fprintf('\nThe signal error before equalization\n'); 47 | fprintf('Bit number num: %d \n', BitErrorNum); 48 | fprintf('SER: %e\n', SymErrorRate); 49 | fprintf('BER: %e\n', BitErrorRate); 50 | 51 | % For the equalized signal 52 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalSignal); 53 | % fprintf('\nThe signal error after equalization\n'); 54 | % fprintf('Bit number num: %d \n', BitErrorNum); 55 | % fprintf('SER: %e\n', SymErrorRate); 56 | % fprintf('BER: %e\n', BitErrorRate); 57 | -------------------------------------------------------------------------------- /Script_50G_FFE_201802.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 25e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | FileDir = '.\Sampled Data\201801\O-band DML\40G PD\obtb\'; 13 | 14 | % TODO import data 15 | 16 | 17 | % BER counting 18 | [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 19 | fprintf('The signal error before equalization\n'); 20 | fprintf('Bit error num: %d\n', BitErrorNum); 21 | fprintf('SER: %e\n', SymErrorRate); 22 | fprintf('BER: %e\n', BitErrorRate); 23 | 24 | % EQ and BER counting 25 | tic 26 | ChannelLen = 151; 27 | alpha = 0.003; 28 | [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen, alpha, 5); 29 | % figure; 30 | % plot(costs); 31 | % title('Curve of Convergence'); 32 | % xlabel('Epoch'); ylabel('Cost'); 33 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 34 | fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen, alpha); 35 | fprintf('Bit error num: %d\n', BitErrorNum); 36 | fprintf('SER: %e\n', SymErrorRate); 37 | fprintf('BER: %e\n', BitErrorRate); 38 | 39 | % ChanLen1st = 121; 40 | % ChanLen2nd = 21; 41 | % ChanLen3rd = 0; 42 | % alpha = 0.003; 43 | % [EqualizedSignal, w, costs] = volterraFFEqualize(ExtractedSignal, OriginalData, 'lms', 10, ChanLen1st, alpha, ChanLen2nd, [], ChanLen3rd); 44 | % figure; 45 | % plot(costs); 46 | % title('Curve of Convergence'); 47 | % xlabel('Epoch'); ylabel('Cost'); 48 | 49 | % EqualizedSignalUS = resample(EqualizedSignal, OverSamplingRatio, 1); 50 | % ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 51 | % step(ed1, EqualizedSignalUS); 52 | % 53 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 54 | % fprintf('Equalization Setup: Volterra LMS Channel Length Setup is [%d %d %d], alpha is %f\n', ChanLen1st, ChanLen2nd, ChanLen3rd, alpha); 55 | % fprintf('Bit error num: %d\n', BitErrorNum); 56 | % fprintf('SER: %e\n', SymErrorRate); 57 | % fprintf('BER: %e\n', BitErrorRate); 58 | toc 59 | -------------------------------------------------------------------------------- /Script_SequenceExtraction.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 25e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | ProjectDir = '.\Sampled Data\201805lt\'; 13 | FileDir = [ProjectDir, '20180519\25G PAM16\soa\btb\prbs15\']; 14 | ROP = -7; 15 | i = 0; 16 | % for ROP = -7 : -2 : -15 17 | for i = 0 : 9 18 | FileName = [num2str(ROP), 'dBm', num2str(i), '.txt']; 19 | FilePath = [FileDir, 'raw\', FileName]; 20 | fprintf(['Processing ', replace(FilePath, '\', '\\'), ' ...\n']); 21 | SampledSignal = importdata(FilePath); 22 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 23 | % plotInFreq(SampledSignal, OSCRate); 24 | SampledSignal = LPF(SampledSignal); 25 | % plotInFreq(SampledSignal, OSCRate); 26 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 27 | 28 | % OriginalData = importdata([ProjectDir, 'pam16_', num2str(i), '.csv']); 29 | OriginalData = importdata([ProjectDir, 'prbs15_pam16.csv']); 30 | OriginalData = (OriginalData - mean(OriginalData)) / std(OriginalData); 31 | 32 | tic 33 | OriginalDataUS = upsample(OriginalData, OverSamplingRatio); 34 | CorrelationResult = conv(SampledSignal, OriginalDataUS(end:-1:1), 'valid'); 35 | figure; 36 | plot(CorrelationResult); 37 | title(FileName); 38 | toc 39 | [a, index] = max(CorrelationResult); 40 | 41 | ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 42 | csvwrite([FileDir, 'filtered\', num2str(ROP), 'dBm', num2str(i), '.csv'], ExtractedSignal); 43 | end 44 | % end 45 | 46 | %% Aux Functions 47 | function y = plotInFreq(in, Fs) 48 | % plotInFreq: plot signal in frequency domain 49 | L = length(in); 50 | Y = fft(in); 51 | M2 = abs(Y / L); 52 | M1 = M2(1 : L/2 + 1); 53 | M1(2 : end - 1) = 2 * M1(2 : end - 1); 54 | P2 = angle(Y / L); 55 | P1 = P2(1 : L/2 + 1) / pi; 56 | f = Fs * (0 : (L / 2)) / L; 57 | figure; 58 | % subplot(2, 1, 1); 59 | plot(f, 20 * log10(M1)) 60 | % subplot(2, 1, 2); 61 | % plot(f, P1) 62 | end 63 | 64 | function y = LPF(x) 65 | % All frequency values are in GHz. 66 | Fs = 80; % Sampling Frequency 67 | Fpass = 18; % Passband Frequency 68 | Fstop = 22; % Stopband Frequency 69 | Dpass = 0.057501127785; % Passband Ripple 70 | Dstop = 0.0001; % Stopband Attenuation 71 | dens = 20; % Density Factor 72 | % Calculate the order from the parameters using FIRPMORD. 73 | [N, Fo, Ao, W] = firpmord([Fpass, Fstop]/(Fs/2), [1 0], [Dpass, Dstop]); 74 | % Calculate the coefficients using the FIRPM function. 75 | b = firpm(N, Fo, Ao, W, {dens}); 76 | Hd = dsp.FIRFilter('Numerator', b); 77 | y = step(Hd,double(x)); 78 | end 79 | -------------------------------------------------------------------------------- /decisionAndCalcBerPAM4.m: -------------------------------------------------------------------------------- 1 | function [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(InputSignal, OriginalData, threshold) 2 | % This function performs the PAM4 decision and bit error ratio calculation for PAM4. 3 | % First the PAM4 decision will be performed and then the error counting and error ratio 4 | % calculation. The %threshold% for PAM4 decision is a optional parameter, which have the 5 | % default value [0.25; 0.5; 0.75], allowing outside to perform some optimization on 6 | % the decision threshold of PAM4. 7 | % 8 | % input: 9 | % InputSignal 10 | % The input signal to be decided and error-calculated. 11 | % OriginalData 12 | % The origin data to be compared to the decided input signal to get the errors. 13 | % threshold (optional) 14 | % The PAM4 decision threshold, which can be adjusted outside this function. 15 | % Default: [0.25; 0.5; 0.75] 16 | % output: 17 | % BitErrorRate 18 | % The bit error ratio of the %InputSignal% comparing to %OriginalData%. 19 | % SymErrorRate 20 | % The symbol error ratio of the %InputSignal% comparing to %OriginalData%. 21 | % BitErrorNum 22 | % The number of bit error, which is 1/sym when only 1 bit changes and 2/sym 23 | % when both bits change. 24 | 25 | %% Parameters Checking 26 | narginchk(2, 3); 27 | 28 | %% Input Signal Normalization 29 | % InputSignal = InputSignal - min(InputSignal); 30 | % InputSignal = InputSignal / max(InputSignal); 31 | InputSignal = (InputSignal - mean(InputSignal)) / std(InputSignal); 32 | OriginalData = OriginalData - min(OriginalData); 33 | OriginalData = (OriginalData / max(OriginalData)) * 3; 34 | 35 | if ~exist('threshold','var') || isempty(threshold) 36 | threshold = zeros(3, 1); 37 | threshold(2) = mean(InputSignal); 38 | threshold(1) = mean(InputSignal(find(InputSignal <= threshold(2)))); 39 | threshold(3) = mean(InputSignal(find(InputSignal > threshold(2)))); 40 | % threshold = [0.25; 0.5; 0.75]; 41 | end 42 | 43 | %% Input Signal Decision 44 | InputSignal(find(InputSignal > threshold(3))) = 3; 45 | InputSignal(find(InputSignal > threshold(2) & InputSignal <= threshold(3))) = 2; 46 | InputSignal(find(InputSignal > threshold(1) & InputSignal <= threshold(2))) = 1; 47 | InputSignal(find(InputSignal <= threshold(1))) = 0; 48 | 49 | %% Error Counting 50 | SymErrorNum = length(find(InputSignal ~= OriginalData)); 51 | % The bit error number is 1/sym when only 1 bit changes and 2/sym when both bits change. 52 | BitErrorNum = SymErrorNum + length(find((InputSignal == 3) & (OriginalData == 1))) ... 53 | + length(find((InputSignal == 2) & (OriginalData == 0))) ... 54 | + length(find((InputSignal == 1) & (OriginalData == 3))) ... 55 | + length(find((InputSignal == 0) & (OriginalData == 2))); 56 | SymErrorRate = SymErrorNum / length(OriginalData); 57 | BitErrorRate = BitErrorNum / (2 * length(OriginalData)); 58 | 59 | OutputSignal = InputSignal; 60 | -------------------------------------------------------------------------------- /Script_playground.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | DataLength = 500000; 9 | SampleRate = 100e9; 10 | SymbolRate = 25e9; 11 | OverSamplingRatio = SampleRate / SymbolRate; 12 | rng('shuffle'); 13 | % Tx signal generation 14 | TxData = zeros(101, 1); 15 | TxData(51) = 1; 16 | TxSignal = reshape(repmat(TxData, 1, OverSamplingRatio)', [], 1); 17 | TxSignal = lowPass10G(TxSignal); 18 | TxSymNum = 1:0.25:101.75; 19 | figure;plot(TxSymNum, TxSignal) 20 | plotInFreq(TxSignal, SampleRate) 21 | 22 | %% Aux Functions 23 | function y = plotInFreq(in, Fs) 24 | % plotInFreq: plot signal in frequency domain 25 | L = length(in); 26 | Y = fft(in); 27 | M2 = abs(Y / L); 28 | M1 = M2(1 : L/2 + 1); 29 | M1(2 : end - 1) = 2 * M1(2 : end - 1); 30 | P2 = angle(Y / L); 31 | P1 = P2(1 : L/2 + 1) / pi; 32 | f = Fs * (0 : (L / 2)) / L; 33 | 34 | figure; 35 | % subplot(2, 1, 1); 36 | plot(f, 20 * log10(M1)) 37 | % subplot(2, 1, 2); 38 | % plot(f, P1) 39 | end 40 | 41 | function y = lowPass10G(x) 42 | % % % 20-order 10G Kaiser LPF 43 | % % % All frequency values are in GHz. 44 | % % Fs = 100; % Sampling Frequency 45 | % % N = 20; % Order 46 | % % Fc = 10; % Cutoff Frequency 47 | % % flag = 'scale'; % Sampling Flag 48 | % % Beta = 1; % Window Parameter 49 | % Hd = dsp.FIRFilter( ... 50 | % 'Numerator', [-6.64318883454626e-18 -0.01856525816569 ... 51 | % -0.0352121682532305 -0.041700902608702 -0.0309951315585328 ... 52 | % 7.94840490930163e-18 0.0486756433241017 0.106687218997798 ... 53 | % 0.161838954883738 0.201391397662279 0.215760491436478 0.201391397662279 ... 54 | % 0.161838954883738 0.106687218997798 0.0486756433241017 ... 55 | % 7.94840490930163e-18 -0.0309951315585328 -0.041700902608702 ... 56 | % -0.0352121682532305 -0.01856525816569 -6.64318883454626e-18]); 57 | 58 | % The following code was used to design the filter coefficients: 59 | % 60 | % N = 20; % Order 61 | % F3dB = 10; % 3-dB Frequency 62 | % Fs = 100; % Sampling Frequency 63 | Hd = dsp.BiquadFilter( ... 64 | 'Structure', 'Direct form II', ... 65 | 'SOSMatrix', [1 2 1 1 -1.54670446522043 0.911831860114908; 1 2 1 1 ... 66 | -1.42280301693279 0.758681247520709; 1 2 1 1 -1.32091343081943 ... 67 | 0.632738792885277; 1 2 1 1 -1.23786474339819 0.530084969790494; 1 2 1 1 ... 68 | -1.1710153071115 0.447454522282603; 1 2 1 1 -1.11823348180364 ... 69 | 0.382212598225574; 1 2 1 1 -1.07784909638848 0.33229475262289; 1 2 1 1 ... 70 | -1.04859957636261 0.29614035756167; 1 2 1 1 -1.0295819074045 ... 71 | 0.272633225955856; 1 2 1 1 -1.02021514617867 0.261055272351718], ... 72 | 'ScaleValues', [0.09128184872362; 0.0839695576469805; ... 73 | 0.0779563405164626; 0.0730550565980766; 0.0691098037927765; ... 74 | 0.0659947791054833; 0.0636114140586013; 0.0618851952997645; ... 75 | 0.060762829637839; 0.0602100315432626; 1]); 76 | y = step(Hd, double(x)); 77 | end 78 | -------------------------------------------------------------------------------- /Script_50G_VolterraSOA.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 25e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | FileName = '.\Sampled Data\50G PAM4\201708\826data\soa-6-10\-14.txt'; 13 | 14 | SampledSignal = importdata(FileName); 15 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 16 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 17 | 18 | ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(SampledSignal), max(SampledSignal)]); 19 | step(ed, SampledSignal); 20 | 21 | % Sequence Extraction 22 | tic 23 | OriginalData = importdata('.\Sampled Data\50G PAM4\201708\826data\data sequence.mat'); 24 | OriginalData = OriginalData'; 25 | OriginalData(find(OriginalData == 1)) = 3; 26 | OriginalData(find(OriginalData == 0.68)) = 1; 27 | OriginalData(find(OriginalData == 0.4)) = -1; 28 | OriginalData(find(OriginalData == 0)) = -3; 29 | CorrelationResult = zeros(length(SampledSignal) - OverSamplingRatio * length(OriginalData) + 1, 1); 30 | parfor i = 1 : length(CorrelationResult) 31 | CorrelationResult(i) = sum(SampledSignal(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 32 | end 33 | figure; 34 | plot(CorrelationResult); 35 | toc 36 | 37 | % BER counting 38 | [a, index] = max(CorrelationResult); 39 | ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 40 | [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 41 | fprintf('The signal error before equalization\n'); 42 | fprintf('Bit error num: %d\n', BitErrorNum); 43 | fprintf('SER: %e\n', SymErrorRate); 44 | fprintf('BER: %e\n', BitErrorRate); 45 | 46 | % EQ and BER counting 47 | tic 48 | % ChannelLen = 101:10:501; 49 | % alpha = [0.001; 0.0003; 0.0001]; 50 | % BER = zeros(length(ChannelLen), length(alpha)); 51 | % BitError = zeros(length(ChannelLen), length(alpha)); 52 | % for i = 1 : length(ChannelLen) 53 | % for j = 1 : length(alpha) 54 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen(i), alpha(j), 5); 55 | % % figure; 56 | % % plot(costs); 57 | % % title('Curve of Convergence'); 58 | % % xlabel('Epoch'); ylabel('Cost'); 59 | % 60 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 61 | % fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen(i), alpha(j)); 62 | % fprintf('Bit error num: %d\n', BitErrorNum); 63 | % fprintf('SER: %e\n', SymErrorRate); 64 | % fprintf('BER: %e\n', BitErrorRate); 65 | % BitError(i, j) = BitErrorNum; 66 | % BER(i, j) = BitErrorRate; 67 | % end 68 | % end 69 | % meshz(log10(alpha), ChannelLen, -log10(BER)); 70 | % title('BER vs ChannelLen & alpha') 71 | % xlabel('log10(alpha)') % x-axis label 72 | % ylabel('ChannelLen') % y-axis label 73 | % zlabel('-log10(BER)') % y-axis label 74 | 75 | ChanLen1st = 121; 76 | ChanLen2nd = 21; 77 | ChanLen3rd = 0; 78 | alpha = 0.003; 79 | [EqualizedSignal, w, costs] = volterraFFEqualize(ExtractedSignal, OriginalData, 'lms', 10, ChanLen1st, alpha, ChanLen2nd, [], ChanLen3rd); 80 | figure; 81 | plot(costs); 82 | title('Curve of Convergence'); 83 | xlabel('Epoch'); ylabel('Cost'); 84 | 85 | EqualizedSignalUS = resample(EqualizedSignal, OverSamplingRatio, 1); 86 | ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 87 | step(ed1, EqualizedSignalUS); 88 | 89 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 90 | fprintf('Equalization Setup: Volterra LMS Channel Length Setup is [%d %d %d], alpha is %f\n', ChanLen1st, ChanLen2nd, ChanLen3rd, alpha); 91 | fprintf('Bit error num: %d\n', BitErrorNum); 92 | fprintf('SER: %e\n', SymErrorRate); 93 | fprintf('BER: %e\n', BitErrorRate); 94 | toc 95 | -------------------------------------------------------------------------------- /mlseEqualize.m: -------------------------------------------------------------------------------- 1 | function [output, ChnlCoeffs, costs] = mlseEqualize(InputSignal, TrainingSignal, ... 2 | TraceBackLen, SamplesPerSymbol, ... 3 | ModFormat, epoch, alpha) 4 | % This function performs equalization based on maximum likelhood sequence estimation. 5 | % First, the InputSignal and TrainingSignal will be normalized to 0-1. 6 | % Then, the inverse linear FFE, which is a channel estimation, is performed 7 | % using a TraceBackLen taps FFE with a learning rate of alpha and training epoch times. 8 | % After training, the MLSE equalization using MATLAB Comm. Toolbox will be performed 9 | % and then the result will be returned. The parameter of MLSE equalizer such as TraceBackLen 10 | % and SamplesPerSymbol can be specified. The ModFormat determine the signal constellation map. 11 | % 12 | % input: 13 | % InputSignal 14 | % The input signal to be equalized. 15 | % TrainingSignal 16 | % The actual signal to be equalized to. 17 | % TraceBackLen (optional) 18 | % The trace back length of mlse equalizer 19 | % Default: 4 20 | % SamplesPerSymbol (optional) 21 | % The numbers of samples per symbol. 22 | % Default: 1 23 | % ModFormat (optional) 24 | % The modulation format: 'PAM4' or 'NRZ'. More modulation format will be added. 25 | % Default: 'PAM4' 26 | % epoch (optional) 27 | % The number of epochs to running in the channel estimation step. 28 | % Default: 5 29 | % alpha (optional) 30 | % The learning rate of LMS algorithm for channel estimation. 31 | % Default: 0.01 32 | % output: 33 | % output 34 | % The equalized signal with the same length of InputSignal 35 | % Size: length(InputSignal), 1 36 | % ChnlCoeffs 37 | % Channel coefficients 38 | % Size: TraceBackLen, 1 39 | % costs 40 | % The costs of channel estimation after each training epoch, which is 41 | % used to draw a curve of convergence and thus determine the best learning rate. 42 | 43 | %% Parameter Checking 44 | narginchk(2, 7); 45 | 46 | if ~exist('TraceBackLen','var') || isempty(TraceBackLen) 47 | TraceBackLen = 4; 48 | end 49 | if ~exist('SamplesPerSymbol','var') || isempty(SamplesPerSymbol) 50 | SamplesPerSymbol = 1; 51 | end 52 | if ~exist('ModFormat','var') || isempty(ModFormat) 53 | ModFormat = 'PAM4'; 54 | end 55 | if ~exist('epoch','var') || isempty(epoch) 56 | epoch = 5; 57 | end 58 | if ~exist('alpha','var') || isempty(alpha) 59 | alpha = 0.01; 60 | end 61 | 62 | %% Signal Normalization and Duplication 63 | % InputSignal and TrainingSignal is normalized to the range between 0-1. 64 | InputSignal = InputSignal - min(InputSignal); 65 | InputSignal = InputSignal / max(InputSignal); 66 | TrainingSignal = TrainingSignal - min(TrainingSignal); 67 | TrainingSignal = TrainingSignal / max(TrainingSignal); 68 | 69 | %% Channel Estimation 70 | % Zero Padding for training signal 71 | TrainingSignalZP = [zeros(TraceBackLen - 1, 1); TrainingSignal]; 72 | 73 | % Weights Initializing 74 | w = zeros(TraceBackLen, 1); 75 | w(TraceBackLen) = 1; 76 | 77 | % Training using LMS learning algorithm 78 | costs = zeros(epoch, 1); 79 | for n = 1 : epoch 80 | for i = 1 : length(TrainingSignalZP) - TraceBackLen + 1 81 | y(i) = w' * TrainingSignalZP(i : i + TraceBackLen - 1); 82 | w = w - alpha * (y(i) - InputSignal(i)) * TrainingSignalZP(i : i + TraceBackLen - 1); 83 | costs(n) = costs(n) + 0.5 * ((y(i) - InputSignal(i)) ^ 2); 84 | end 85 | % Record the cost/error of each epoch 86 | costs(n) = costs(n) / (length(TrainingSignalZP) - TraceBackLen + 1); 87 | end 88 | 89 | % Reverse w to get channel coefficients 90 | ChnlCoeffs = w(end:-1:1); 91 | 92 | %% MLSE equalization using MATLAB Commmunication Toolbox 93 | % Generating constellation map for different modulation format 94 | if ModFormat == 'PAM4' 95 | % const = [1/8; 3/8; 5/8; 7/8]; 96 | const = [0; 1/3; 2/3; 1]; 97 | elseif ModFormat == 'NRZ' 98 | const = [1/4; 3/4]; 99 | end 100 | % Performing the mlse equalization 101 | output = mlseeq(InputSignal, ChnlCoeffs, const, TraceBackLen, 'rst', SamplesPerSymbol); 102 | -------------------------------------------------------------------------------- /lowPassFilter25G.m: -------------------------------------------------------------------------------- 1 | function y = lowPassFilter25G(x) 2 | %LOWPASSFILTER25G Filters input x and returns output y. 3 | 4 | % MATLAB Code 5 | % Generated by MATLAB(R) 9.1 and the DSP System Toolbox 9.3. 6 | % Generated on: 13-Jul-2017 22:48:59 7 | 8 | %#codegen 9 | 10 | % To generate C/C++ code from this function use the codegen command. Type 11 | % 'help codegen' for more information. 12 | 13 | persistent Hd; 14 | 15 | if isempty(Hd) 16 | 17 | % The following code was used to design the filter coefficients: 18 | % % Equiripple Lowpass filter designed using the FIRPM function. 19 | % 20 | % % All frequency values are in GHz. 21 | % Fs = 600; % Sampling Frequency 22 | % 23 | % Fpass = 20; % Passband Frequency 24 | % Fstop = 30; % Stopband Frequency 25 | % Dpass = 0.057501127785; % Passband Ripple 26 | % Dstop = 0.001; % Stopband Attenuation 27 | % dens = 20; % Density Factor 28 | % 29 | % % Calculate the order from the parameters using FIRPMORD. 30 | % [N, Fo, Ao, W] = firpmord([Fpass, Fstop]/(Fs/2), [1 0], [Dpass, Dstop]); 31 | % 32 | % % Calculate the coefficients using the FIRPM function. 33 | % b = firpm(N, Fo, Ao, W, {dens}); 34 | 35 | Hd = dsp.FIRFilter( ... 36 | 'Numerator', [0.00056252330752353 -0.0002210699675065 ... 37 | -0.000357791147890221 -0.00058852303901478 -0.000906321768734106 ... 38 | -0.00130462862798463 -0.00177406750290929 -0.00230060803688159 ... 39 | -0.00286434668282593 -0.00343965535922648 -0.00399546695780133 ... 40 | -0.00449644573328443 -0.00490475410904657 -0.00518219294711546 ... 41 | -0.00529266498698172 -0.00520490246185537 -0.00489514846401471 ... 42 | -0.00434978482818447 -0.00356791707284706 -0.00256297582194769 ... 43 | -0.00136381207804231 -1.46145494606937e-05 0.00142580929481935 ... 44 | 0.00288769921453774 0.00428741821222042 0.00553991682726253 ... 45 | 0.00655703682292805 0.00725515572628666 0.0075608221616118 ... 46 | 0.00741622893177966 0.00678416096763582 0.00565304169065939 ... 47 | 0.00403988116652243 0.00199257404417469 -0.000409593802526702 ... 48 | -0.00305725891938249 -0.00581414174684957 -0.00852193563599587 ... 49 | -0.0110069638070947 -0.0130880280699824 -0.0145852087202439 ... 50 | -0.0153295315840698 -0.0151724476883366 -0.0139950296441678 ... 51 | -0.0117160505259669 -0.00829871393213389 -0.00375440281280911 ... 52 | 0.00185289189501805 0.00840878885862349 0.0157500658890755 ... 53 | 0.0236700485360048 0.0319263723034723 0.0402508829153542 ... 54 | 0.0483601438686822 0.0559687845249519 0.0628015937513899 ... 55 | 0.0686062254188838 0.0731648913908242 0.0763045783699315 ... 56 | 0.0779051527315739 0.0779051527315739 0.0763045783699315 ... 57 | 0.0731648913908242 0.0686062254188838 0.0628015937513899 ... 58 | 0.0559687845249519 0.0483601438686822 0.0402508829153542 ... 59 | 0.0319263723034723 0.0236700485360048 0.0157500658890755 ... 60 | 0.00840878885862349 0.00185289189501805 -0.00375440281280911 ... 61 | -0.00829871393213389 -0.0117160505259669 -0.0139950296441678 ... 62 | -0.0151724476883366 -0.0153295315840698 -0.0145852087202439 ... 63 | -0.0130880280699824 -0.0110069638070947 -0.00852193563599587 ... 64 | -0.00581414174684957 -0.00305725891938249 -0.000409593802526702 ... 65 | 0.00199257404417469 0.00403988116652243 0.00565304169065939 ... 66 | 0.00678416096763582 0.00741622893177966 0.0075608221616118 ... 67 | 0.00725515572628666 0.00655703682292805 0.00553991682726253 ... 68 | 0.00428741821222042 0.00288769921453774 0.00142580929481935 ... 69 | -1.46145494606937e-05 -0.00136381207804231 -0.00256297582194769 ... 70 | -0.00356791707284706 -0.00434978482818447 -0.00489514846401471 ... 71 | -0.00520490246185537 -0.00529266498698172 -0.00518219294711546 ... 72 | -0.00490475410904657 -0.00449644573328443 -0.00399546695780133 ... 73 | -0.00343965535922648 -0.00286434668282593 -0.00230060803688159 ... 74 | -0.00177406750290929 -0.00130462862798463 -0.000906321768734106 ... 75 | -0.00058852303901478 -0.000357791147890221 -0.0002210699675065 ... 76 | 0.00056252330752353]); 77 | end 78 | 79 | y = step(Hd,double(x)); 80 | 81 | 82 | % [EOF] 83 | -------------------------------------------------------------------------------- /Script_50G_NoSOA.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 28e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | FileDir = '.\Sampled Data\50G PAM4\201709\56gpam4_10dbm\obtb\'; 13 | % ROP = -22 : -12; 14 | % ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(SampledSignal), max(SampledSignal)]); 15 | % step(ed, SampledSignal); 16 | % BER = zeros(length(ROP), 5); 17 | % BitError = zeros(length(ROP), 5); 18 | % i = 1; 19 | % for i = 1 : length(ROP) 20 | FileName = ['C2-12dBm00000.dat']; 21 | % FileName = [num2str(ROP(i)), 'dBm0.csv']; 22 | % FileName = '-10.txt'; 23 | SampledSignal = importdata([FileDir, FileName]); 24 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 25 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 26 | % % Sequence Extraction 27 | tic 28 | OriginalSignal = importdata('.\Original Data\Original_Data.txt'); 29 | OriginalSignal = (OriginalSignal - 0.5) * 2; 30 | OriginalData_port1 = OriginalSignal; 31 | shiftnum = 19732; 32 | OriginalData_port2 = [-(OriginalSignal(end - shiftnum + 1 : end)); 33 | -(OriginalSignal(1 : end - shiftnum))]; 34 | OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 35 | % OriginalData = OriginalData_port1; 36 | OriginalDataUS = upsample(OriginalData, OverSamplingRatio); 37 | CorrelationResult = conv(SampledSignal, OriginalDataUS(end:-1:1), 'valid'); 38 | figure; 39 | plot(CorrelationResult); 40 | title(FileName); 41 | toc 42 | [a, index] = max(CorrelationResult); 43 | ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 44 | 45 | % BER counting 46 | [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 47 | fprintf('The signal error before equalization\n'); 48 | fprintf('Bit error num: %d\n', BitErrorNum); 49 | fprintf('SER: %e\n', SymErrorRate); 50 | fprintf('BER: %e\n', BitErrorRate); 51 | % BER(i, 5) = BitErrorRate; 52 | % BitError(i, 5) = BitErrorNum; 53 | 54 | % EQ and BER counting 55 | tic 56 | ChannelLen = 87; 57 | alpha = 0.0067; 58 | % for j = 1 : length(ChannelLen) 59 | [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen, alpha, 10); 60 | % figure; 61 | % plot(costs); 62 | % title('Curve of Convergence'); 63 | % xlabel('Epoch'); ylabel('Cost'); 64 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 65 | fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen, alpha); 66 | fprintf('Bit error num: %d\n', BitErrorNum); 67 | fprintf('SER: %e\n', SymErrorRate); 68 | fprintf('BER: %e\n', BitErrorRate); 69 | % BER(i, j) = BitErrorRate; 70 | % BitError(i, j) = BitErrorNum; 71 | % end 72 | toc 73 | 74 | % tic 75 | % ChanLen1st = 121; 76 | % ChanLen2nd = 21; 77 | % ChanLen3rd = 5; 78 | % alpha = 0.003; 79 | % [EqualizedSignal, w, costs] = volterraFFEqualize(ExtractedSignal, OriginalData, 'lms', 10, ChanLen1st, alpha, ChanLen2nd, [], ChanLen3rd); 80 | % figure; 81 | % plot(costs); 82 | % title('Curve of Convergence'); 83 | % xlabel('Epoch'); ylabel('Cost'); 84 | % 85 | % EqualizedSignalUS = resample(EqualizedSignal, OverSamplingRatio, 1); 86 | % ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 87 | % step(ed1, EqualizedSignalUS); 88 | % 89 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 90 | % fprintf('Equalization Setup: Volterra LMS Channel Length Setup is [%d %d %d], alpha is %f\n', ChanLen1st, ChanLen2nd, ChanLen3rd, alpha); 91 | % fprintf('Bit error num: %d\n', BitErrorNum); 92 | % fprintf('SER: %e\n', SymErrorRate); 93 | % fprintf('BER: %e\n', BitErrorRate); 94 | % toc 95 | % end 96 | -------------------------------------------------------------------------------- /generateData.m: -------------------------------------------------------------------------------- 1 | function OriginalData = generateData(PAM4Flag, NewPRBSGenerationFlag) 2 | % This function generate NRZ or PAM4 data from PRBS generator or local file 3 | % and then generate the data with synchronization header which is then 4 | % loaded to PPG. 5 | % 6 | % input:(input parameter are all optional, the order of args follows the order below) 7 | % PAM4Flag 8 | % The flag for PAM4 generation: 9 | % 1: enable PAM4 generation 10 | % 0: disable PAM4 generation and output NRZ signal 11 | % Default value: 1 12 | % NewPRBSGenerationFlag 13 | % The flag for regenerating prbs data: 14 | % 1: regenerating new data 15 | % 0: load prbs data from ./Original Data/Original_Data_4096.txt 16 | % Note that if the ./Original Data/Original_Data_4096 doesn't exist, 17 | % the program will automatically generate new data whether this flag 18 | % is set to 1 or 0. 19 | % Default value: 0 20 | % output: 21 | % OriginalData 22 | % The original PAM4 or NRZ data without synchronization header in row vector. 23 | % Size: OriginalDataLength, 1 24 | 25 | narginchk(0,2); 26 | 27 | if ~exist('PAM4Flag','var') || isempty(PAM4Flag) 28 | PAM4Flag = 1; 29 | end 30 | 31 | if ~exist('NewPRBSGenerationFlag','var') || isempty(NewPRBSGenerationFlag) 32 | NewPRBSGenerationFlag = 0; 33 | end 34 | 35 | % change the current directory to the folder which contains this m file 36 | cd(fileparts(which(mfilename))); 37 | 38 | %% define parameter 39 | PathToOriginalData = '.\Original Data\'; % relative path 40 | OriginalDataFileName = 'Original_Data'; 41 | 42 | if exist('.\Original Data', 'dir') == 0 43 | mkdir('Original Data'); 44 | end 45 | 46 | %% PRBS generation 47 | if (NewPRBSGenerationFlag == 1) || (exist(strcat(PathToOriginalData, OriginalDataFileName, '.txt'), 'file') == 0) 48 | % h = commsrc.pattern('SamplingFrequency', 10000, ... 49 | % 'SamplesPerSymbol', 1, ... 50 | % 'PulseType', 'NRZ', ... 51 | % 'OutputLevels', [0 1], ... 52 | % 'RiseTime', 0, ... 53 | % 'FallTime', 0, ... 54 | % 'DataPattern', 'PRBS12'); % set the prbs pattern 55 | % OriginalData = generate(h, OriginalDataLength); 56 | % OriginalData = double(xor(1, OriginalData)); 57 | % Generating PRBS15 58 | fbconnection = [0,0,0,0,0,0,0,0,0,0,0,0,0,1,1]; 59 | n = length(fbconnection); 60 | N = 2 ^ n - 1; 61 | OriginalData = zeros(N, 1); 62 | register = [zeros(1, n - 1), 1]; 63 | OriginalData(1) = register(n); 64 | for i = 2 : N 65 | newregister(1) = mod(sum(fbconnection .* register), 2); 66 | for j = 2 : n 67 | newregister(j) = register(j - 1); 68 | end 69 | register = newregister; 70 | OriginalData(i) = register(n); 71 | end 72 | 73 | % save the prbs data to .\Original Data\Original_Data.txt 74 | fid = fopen(strcat(PathToOriginalData, OriginalDataFileName, '.txt'), 'w'); 75 | fprintf(fid, '%d\r\n', OriginalData); 76 | fclose(fid); 77 | end 78 | OriginalData = importdata(strcat(PathToOriginalData, OriginalDataFileName, '.txt')); 79 | 80 | %% PAM4 generation 81 | % use 2 same prbs data, delay one with 2 sym, multiply the other one with 2 82 | % and then add the 2 data to form PAM4 data 83 | if PAM4Flag == 1 84 | OriginalData_port1 = OriginalData; 85 | % Day 2 Setup 86 | % shiftnum = 6; 87 | % OriginalData_port2 = [~(OriginalData(end - shiftnum + 1 : end)); 88 | % ~(OriginalData(1 : end - shiftnum))]; 89 | % OriginalData = OriginalData_port1 + 2 * OriginalData_port2; 90 | 91 | % 20170428 Setup 92 | % shiftnum = 6; 93 | % OriginalData_port2 = [~(OriginalData(shiftnum + 1 : end)); 94 | % ~(OriginalData(1 : shiftnum))]; 95 | % OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 96 | 97 | % 20170606 RoF Setup 98 | % shiftnum = 67; 99 | % OriginalData_port2 = [~(OriginalData(shiftnum + 1 : end)); 100 | % ~(OriginalData(1 : shiftnum))]; 101 | % OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 102 | 103 | % 20170608 RoF Setup 104 | shiftnum = 61; 105 | OriginalData_port2 = [~(OriginalData(end - shiftnum + 1 : end)); 106 | ~(OriginalData(1 : end - shiftnum))]; 107 | OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 108 | 109 | % save the PAM4 data to .\Original Data\Original_Data_4096_PAM4.txt 110 | fid = fopen(strcat(PathToOriginalData, OriginalDataFileName, '_PAM4.txt'), 'w'); 111 | fprintf(fid, '%d\r\n', OriginalData); 112 | fclose(fid); 113 | end 114 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # README 2 | 3 | ## 这是什么 4 | 5 | 计划是实现一个用于通信的rx dsp库，其中包含数据产生、信号同步提取、均衡算法和判决与误码计算等代码。 6 | 7 | ## 尚未完成 8 | 9 | * 该readme的编写 10 | * 新的均衡算法的实现 11 | * Linear DFE 12 | * Volterra DFE 13 | * 一些Machine Learning算法 14 | * NRZ判决与误码计算 15 | * 用Python实现 16 | 17 | ## 核心函数 18 | 19 | ###信号处理辅助函数 20 | 21 | ```OriginalData = generateData(OriginalDataLength, PAM4Flag, NewPRBSGenerationFlag, SyncZerosLength)``` 22 | 23 | 产生数据函数。该函数可产生源数据，供其他函数以使用。此外，该函数还可以产生用于载入PPG发送的数据。目前可选产生NRZ或者PAM4数据。 24 | 25 | * 输入参数 26 | 27 | 1. OriginalDataLength（可选） 28 | 29 | 所产生的原始数据长度，默认为4096； 30 | 31 | 2. PAM4Flag（可选） 32 | 33 | 是否产生PAM4的标志：0为产生NRZ，1为产生PAM4。默认为1。 34 | 35 | 3. NewPRBSGenerationFlag（可选） 36 | 37 | 是否重新生成数据的标志：0为重新生成，1为读取./Original Data/Original_Data_4096.txt中的数据。默认为0。 38 | 39 | 4. SyncZerosLength（可选） 40 | 41 | 所产生给PPG的数据中同步部分0的个数，默认为50。 42 | 43 | * 输出参数 44 | 45 | OriginalData 46 | 47 | 所产生或从文件中读取的NRZ或者PAM4源数据。 48 | 49 | ```[ExtractedSignalUS, OriginalSignalUS] = syncAndExtractSignal(SampledData, OriginalData, OverSamplingRatio, UpSamplingRatio)``` 50 | 51 | 同步与信号提取函数。该函数可将所接收到的信号从DSO采集到的数据SampledData中同步并提取出来。 52 | 53 | * 输入参数 54 | 55 | 1. SampledData 56 | 57 | 从DSO来的采样数据 58 | 59 | 2. OriginalData 60 | 61 | 原始发送数据 62 | 63 | 3. OverSamplingRatio 64 | 65 | 过采样率，通常等于DSO采样率/信号速率 66 | 67 | 4. UpSamplingRatio（可选） 68 | 69 | 输出信号上采样率。默认值为1，即为不进行上采样。 70 | 71 | * 输出参数 72 | 73 | 1. ExtractedSignalUS 74 | 75 | 从SampledData中同步提取出来的信号，经过UpSamplingRatio倍上采样，是一个length(OriginalData)*UpSamplingRatio, 1的列向量。 76 | 77 | 2. OriginalSignalUS 78 | 79 | 经过UpSamplingRatio倍上采样的原始数据，也是一个length(OriginalData)*UpSamplingRatio, 1的列向量。 80 | 81 | ```[BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(InputSignal, OriginalData, threshold)``` 82 | 83 | PAM4判决与误码计算函数。 84 | 85 | * 输入参数 86 | 87 | 1. InputSignal 88 | 89 | 待判决的输入信号 90 | 91 | 2. OriginalData 92 | 93 | 用于计算误码的原始数据 94 | 95 | 3. threshold（可选） 96 | 97 | PAM4判决电平，默认为[0.25; 0.5; 0.75]。 98 | 99 | * 输出参数 100 | 101 | 1. BitErrorRate 102 | 103 | 误比特率。在计算误码率时，会计算错误一个符号时反转的比特数。 104 | 105 | 2. SymErrorRate 106 | 107 | 误符号率 108 | 109 | 3. BitErrorNum 110 | 111 | 误比特数 112 | 113 | ###均衡函数 114 | 115 | ```[output, w, costs] = linearFFEqualize(InputSignal, TrainingSignal, AlgType, FFETaps, alpha, epoch)``` 116 | 117 | linear FFE均衡函数。支持LMS和RLS两种算法。输入信号和训练信号会先被归一化到0-1。在目前的实现中所有的InputSignal都会被用于训练epoch次，在训练后会使用训练好的权重进行均衡。 118 | 119 | * 输入参数 120 | 121 | 1. InputSignal 122 | 123 | 将被均衡的输入信号。 124 | 125 | 2. TrainingSignal 126 | 127 | 用于训练的目标序列。 128 | 129 | 3. AlgType（可选） 130 | 131 | ‘lms’代表LMS算法，‘rls’代表RLS算法。默认为使用LMS算法。 132 | 133 | 4. FFETaps（可选） 134 | 135 | FFE抽头数量，需为奇数，默认为5。 136 | 137 | 5. alpha（可选） 138 | 139 | 学习速率。默认为0.01。 140 | 141 | 6. epoch（可选） 142 | 143 | 训练次数。默认为1。 144 | 145 | * 输出参数 146 | 147 | 1. output 148 | 149 | 均衡后的信号。一个length(InputSignal), 1的列向量。 150 | 151 | 2. w 152 | 153 | 训练好的权重。一个FFETaps, 1的列向量。 154 | 155 | 3. costs 156 | 157 | 每个训练epoch的cost向量，用于画收敛曲线以表现学习效果。 158 | 159 | 160 | ```[output, w, costs] = volLMSFFEqualize(InputSignal, TrainingSignal, chanLen, alpha1st, epoch, en2ndOrder, en3rdOrder, alpha2nd, alpha3rd)``` 161 | 162 | volterra FFE均衡函数。目前仅支持LMS算法，之后会增加对RLS算法的支持。输入信号和训练信号会先被归一化到0-1。在目前的实现中所有的InputSignal都会被用于训练epoch次，在训练后会使用训练好的权重进行均衡。 163 | 164 | * 输入参数 165 | 166 | 1. InputSignal 167 | 168 | 将被均衡的输入信号。 169 | 170 | 2. TrainingSignal 171 | 172 | 用于训练的目标序列。 173 | 174 | 3. chanLen（可选） 175 | 176 | 信道长度。必须为奇数。默认为5。 177 | 178 | 4. alpha1st（可选） 179 | 180 | volterra 1阶核的学习速率。默认为0.01。 181 | 182 | 5. epoch（可选） 183 | 184 | 训练次数。默认为1。 185 | 186 | 6. en2ndOrder（可选） 187 | 188 | 使用volterra 2阶核。默认为true。 189 | 190 | 7. en3rdOrder（可选） 191 | 192 | 使用volterra 3阶核。默认为true。 193 | 194 | 8. alpha2nd（可选） 195 | 196 | volterra 2阶核的学习速率。默认与alpha1st相等。 197 | 198 | 9. alpha3rd（可选） 199 | 200 | volterra 3阶核的学习速率。默认与alpha1st相等。 201 | 202 | * 输出参数 203 | 204 | 1. output 205 | 206 | 均衡后的信号。一个length(InputSignal), 1的列向量。 207 | 208 | 2. w 209 | 210 | 训练好的权重。一个chanLen + kernel2ndSize + kernel3rdSize, 1的列向量。 211 | 212 | 3. costs 213 | 214 | 每个训练epoch的cost向量，用于画收敛曲线以表现学习效果。 215 | 216 | 217 | ## 一些例子 218 | 219 | ```LMSFFEqualizationExample.m``` 220 | 221 | 这个例子展示了如何使用本库进行信号、同步、线性LMS均衡和判决的基本workflow。 222 | 223 | ```LMSFFEqualizationExample_2.m``` 224 | 225 | 这个例子展示了如何对整个文件夹中所采集到的dat文件进行遍历以画出BER曲线。同样使用线性LMS均衡器。 -------------------------------------------------------------------------------- /linearFFEqualize.m: -------------------------------------------------------------------------------- 1 | function [output, w, costs] = linearFFEqualize(InputSignal, TrainingSignal, AlgType, ... 2 | FFETaps, alpha, epoch) 3 | % This function performs the feed forward equalization with LMS or RLS algorithm. 4 | % First, the InputSignal and TrainingSignal will be normalized to 0-1. 5 | % The training will use all the InputSignal and will be performed epoch times. 6 | % The alpha is the learning rate of LMS or the forgetting factor of RLS, 7 | % which should be chosen carefully with the help of curve of convergence. 8 | % After training, the equalization will be performed and then the result will 9 | % be returned. 10 | % 11 | % input: 12 | % InputSignal 13 | % The input signal to be equalized. 14 | % TrainingSignal 15 | % The actual signal to be equalized to. 16 | % AlgType 17 | % 'lms' for LMS or 'rls' for RLS. 18 | % FFETaps (optional) 19 | % The numbers of FFE taps which must be odd. 20 | % Default: 5 21 | % alpha (optional) 22 | % The learning rate of LMS algorithm or the forgetting factor of RLS. 23 | % Default: 0.01 for AlgType = 'lms', 0.99 for AlgType = 'rls' 24 | % epoch (optional) 25 | % The epoch of the learning of LMS through all the input signal. 26 | % Default: 1 27 | % output: 28 | % output 29 | % The equalized signal with the same length of InputSignal 30 | % Size: length(InputSignal), 1 31 | % w 32 | % Weights of FFE 33 | % Size: FFETaps, 1 34 | % costs 35 | % The costs after each training epoch, which is used to draw a 36 | % curve of convergence and thus determine the best learning rate. 37 | 38 | %% Parameter Checking 39 | narginchk(3, 6); 40 | 41 | if ~exist('FFETaps','var') || isempty(FFETaps) 42 | FFETaps = 5; 43 | end 44 | 45 | if ~exist('alpha','var') || isempty(alpha) 46 | if AlgType == 'lms' 47 | alpha = 0.01; 48 | elseif AlgType == 'rls' 49 | alpha = 0.99; 50 | end 51 | end 52 | 53 | if ~exist('epoch','var') || isempty(epoch) 54 | epoch = 1; 55 | end 56 | 57 | % TODO add some parameter checking 58 | if mod(FFETaps, 2) == 0 59 | error('linearFeedForwardEqualize:argChk', 'FFE taps must be odd'); 60 | end 61 | 62 | if ~strcmp(AlgType, 'lms') && ~strcmp(AlgType, 'rls') 63 | error('linearFeedForwardEqualize:argChk', 'AlgType must be lms or rls'); 64 | end 65 | 66 | if FFETaps <= 0 67 | error('linearFeedForwardEqualize:argChk', 'FFE taps must be bigger than 0'); 68 | end 69 | 70 | %% Signal Normalization and Duplication 71 | % InputSignal and TrainingSignal is normalized to the range between 0-1. 72 | InputSignal = InputSignal - min(InputSignal); 73 | InputSignal = InputSignal / max(InputSignal); 74 | TrainingSignal = TrainingSignal - min(TrainingSignal); 75 | TrainingSignal = TrainingSignal / max(TrainingSignal); 76 | 77 | % Both signal is duplicated for better performance 78 | InputSignalDup = repmat(InputSignal, 2, 1); 79 | TrainingSignalDup = repmat(TrainingSignal, 2, 1); 80 | % Zero Padding for input signal 81 | InputSignalZP = [InputSignalDup(end-floor(FFETaps/2)+1 : end); InputSignalDup; InputSignalDup(1:floor(FFETaps/2))]; 82 | 83 | %% Weights Initializing 84 | % TODO choose on : randomly init weights or init to 0 85 | w = zeros(FFETaps, 1); 86 | w(floor(length(w)/2) + 1) = 1; 87 | % rng('shuffle'); 88 | % w = rand(FFETaps, 1); 89 | % w = 2 * w - 1; 90 | 91 | %% Training 92 | costs = zeros(epoch, 1); 93 | y = zeros(size(TrainingSignalDup)); 94 | if AlgType == 'lms' 95 | % The LMS learning algorithm 96 | for n = 1 : epoch 97 | for i = 1 : length(InputSignalZP) - FFETaps + 1 98 | y(i) = w' * InputSignalZP(i : i + FFETaps - 1); 99 | w = w - alpha * (y(i) - TrainingSignalDup(i)) * InputSignalZP(i : i + FFETaps - 1); 100 | costs(n) = costs(n) + 0.5 * ((y(i) - TrainingSignalDup(i)) ^ 2); 101 | end 102 | % Record the cost/error of each epoch 103 | costs(n) = costs(n) / (length(InputSignalZP) - FFETaps + 1); 104 | end 105 | elseif AlgType == 'rls' 106 | % The RLS learning algorithm 107 | Sd = eye(FFETaps); 108 | for n = 1 : epoch 109 | for i = 1 : length(InputSignalZP) - FFETaps + 1 110 | x = InputSignalZP(i : i + FFETaps - 1); 111 | e = TrainingSignalDup(i) - w' * x; 112 | phi = Sd * x; 113 | Sd = (1 / alpha) * (Sd - (phi * phi') / (alpha + phi' * x)); 114 | w = w + e * Sd * x; 115 | costs(n) = costs(n) + 0.5 * (e ^ 2); 116 | end 117 | % Record the cost/error of each epoch 118 | costs(n) = costs(n) / (length(InputSignalZP) - FFETaps + 1); 119 | end 120 | end 121 | 122 | %% Using Trained Weights to Equalize Data 123 | for i = 1 : length(InputSignalZP) - FFETaps + 1 124 | y(i) = w' * InputSignalZP(i : i + FFETaps - 1); 125 | end 126 | 127 | % TODO choose a half of the output 128 | output = y(1 : length(y) / 2); 129 | % output = y(length(y) / 2 + 1 : end); 130 | -------------------------------------------------------------------------------- /Script_50G_SequenceExtraction.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | OSCRate = 80e9; 9 | DataRate = 33e9; 10 | SampleRate = lcm(OSCRate, DataRate); 11 | OverSamplingRatio = SampleRate / DataRate; 12 | ProjectDir = '.\Sampled Data\201805lt\'; 13 | FileDir = [ProjectDir, 'O-band\33G PAM8\btb\prbs15\']; 14 | % FileDir = '.\Sampled Data\50G PAM4\201709\56gpam4_10dbm\25km\'; 15 | % ROP = -22:-13; 16 | ROP = -11; 17 | i = 0; 18 | % for ROP = -19 : 2 : -11 19 | % for i = 0 : 4 20 | % FileName = ['C2', num2str(ROP), 'dBm0000', num2str(i), '.dat']; 21 | FileName = [num2str(ROP), '_', num2str(i), '.txt']; 22 | FilePath = [FileDir, 'raw\', FileName]; 23 | fprintf(['Processing ', replace(FilePath, '\', '\\'), ' ...\n']); 24 | SampledSignal = importdata(FilePath); 25 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 26 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 27 | mean(SampledSignal) 28 | std(SampledSignal) 29 | 30 | OriginalData = importdata([ProjectDir, 'pam16_', num2str(i), '.csv']); 31 | % OriginalData = importdata([ProjectDir, 'prbs15_pam16.csv']); 32 | % OriginalData = importdata('.\Sampled Data\50G PAM4\201709\56gpam4_10dbm\25km\extracted\prbs15pam4.csv'); 33 | % OriginalData = OriginalData'; 34 | % OriginalData = 3 - OriginalData; 35 | % csvwrite([FileDir, 'extracted\pam4_', num2str(i), '.csv'], OriginalData); 36 | OriginalData = (OriginalData - mean(OriginalData)) / std(OriginalData); 37 | % OriginalSignal = (OriginalSignal - 0.5) * 2; 38 | % OriginalData_port1 = OriginalSignal; 39 | % shiftnum = 13035; 40 | % OriginalData_port2 = [-(OriginalSignal(shiftnum + 1 : end)); 41 | % -(OriginalSignal(1 : shiftnum))]; 42 | % OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 43 | % tic 44 | % CorrelationResult = zeros(length(SampledSignal) - OverSamplingRatio * length(OriginalData) + 1, 1); 45 | % parfor i = 1 : length(CorrelationResult) 46 | % CorrelationResult(i) = sum(SampledSignal(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 47 | % end 48 | % figure; 49 | % plot(CorrelationResult); 50 | % title(FileName); 51 | % toc 52 | 53 | plotInFreq(SampledSignal, SampleRate); 54 | % SampledSignal = lowPass10G(SampledSignal); 55 | % SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 56 | % plotInFreq(SampledSignal, SampleRate); 57 | 58 | % % TODO remove this 59 | % ed0 = comm.EyeDiagram('DisplayMode', '2D color histogram', 'OversamplingMethod', 'Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(SampledSignal), max(SampledSignal)]); 60 | % step(ed0, SampledSignal); 61 | % % TODO Matched Filtering 62 | % SampledSignal = filter(ones(OverSamplingRatio, 1), 1, SampledSignal); 63 | % SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 64 | % plotInFreq(SampledSignal, SampleRate); 65 | 66 | % tic 67 | % OriginalDataUS = upsample(OriginalData, OverSamplingRatio); 68 | % CorrelationResult = conv(SampledSignal, OriginalDataUS(end:-1:1), 'valid'); 69 | % figure; 70 | % plot(CorrelationResult); 71 | % title(FileName); 72 | % toc 73 | % [a, index] = max(CorrelationResult); 74 | 75 | % ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 76 | % csvwrite([FileDir, 'extracted\', num2str(ROP), 'dBm', num2str(i), '.csv'], ExtractedSignal); 77 | 78 | % ExtractedSignal = SampledSignal(index : index + length(OriginalData) * OverSamplingRatio - 1); 79 | % ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Histogram interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(ExtractedSignal), max(ExtractedSignal)]); 80 | % step(ed, ExtractedSignal); 81 | % end 82 | % end 83 | 84 | %% Aux Functions 85 | function y = plotInFreq(in, Fs) 86 | % plotInFreq: plot signal in frequency domain 87 | L = length(in); 88 | Y = fft(in); 89 | M2 = abs(Y / L); 90 | M1 = M2(1 : L/2 + 1); 91 | M1(2 : end - 1) = 2 * M1(2 : end - 1); 92 | P2 = angle(Y / L); 93 | P1 = P2(1 : L/2 + 1) / pi; 94 | f = Fs * (0 : (L / 2)) / L; 95 | 96 | figure; 97 | % subplot(2, 1, 1); 98 | plot(f, 20 * log10(M1)) 99 | % subplot(2, 1, 2); 100 | % plot(f, P1) 101 | end 102 | 103 | function y = lowPass10G(x) 104 | % All frequency values are in GHz. 105 | Fs = 560; % Sampling Frequency 106 | Fpass = 10; % Passband Frequency 107 | Fstop = 15; % Stopband Frequency 108 | Dpass = 0.057501127785; % Passband Ripple 109 | Dstop = 0.01; % Stopband Attenuation 110 | flag = 'scale'; % Sampling Flag 111 | % Calculate the order from the parameters using KAISERORD. 112 | [N, Wn, BETA, TYPE] = kaiserord([Fpass Fstop]/(Fs/2), [1 0], [Dstop Dpass]); 113 | % Calculate the coefficients using the FIR1 function. 114 | b = fir1(N, Wn, TYPE, kaiser(N+1, BETA), flag); 115 | Hd = dsp.FIRFilter('Numerator', b); 116 | y = step(Hd,double(x)); 117 | end 118 | -------------------------------------------------------------------------------- /Script_RoF_Wireless_SquareLawDetection.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | RawSignal = importdata('.\Sampled Data\RoF\wireless\40km\20170801_2_4Vpp\C3rof 6-5dBm00000.dat'); 9 | 10 | SampleRate = 600e9; 11 | OSCRate = 120e9; 12 | DataRate = 12.5e9; 13 | OverSamplingRatio = SampleRate / DataRate; 14 | 15 | SampledSignal = (RawSignal(:, 1) - mean(RawSignal(:, 1))) / std(RawSignal(:, 1)); 16 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 17 | 18 | t = (0:length(SampledSignal)-1)*(1/SampleRate); 19 | t = t'; 20 | 21 | index = find( (t < 22e-9) & (t > 2e-9)); 22 | 23 | X = SampledSignal; 24 | L = length(X); 25 | Y = fft(X); 26 | P2 = abs(Y/L); 27 | P1 = P2(1:L/2+1); 28 | P1(2:end-1) = 2*P1(2:end-1); 29 | Fs = SampleRate; 30 | f = Fs*(0:(L/2))/L; 31 | figure; 32 | plot(f,20 * log10(P1)) 33 | 34 | r = bandPassFilter12G38G(SampledSignal); 35 | 36 | figure; 37 | plot(t(index), r(index)) 38 | 39 | X = r; 40 | L = length(X); 41 | Y = fft(X); 42 | P2 = abs(Y/L); 43 | P1 = P2(1:L/2+1); 44 | P1(2:end-1) = 2*P1(2:end-1); 45 | Fs = SampleRate; 46 | f = Fs*(0:(L/2))/L; 47 | figure; 48 | plot(f,20 * log10(P1)) 49 | 50 | r = r .^ 2; 51 | 52 | X = r; 53 | L = length(X); 54 | Y = fft(X); 55 | P2 = abs(Y/L); 56 | P1 = P2(1:L/2+1); 57 | P1(2:end-1) = 2*P1(2:end-1); 58 | Fs = SampleRate; 59 | f = Fs*(0:(L/2))/L; 60 | figure; 61 | plot(f,20 * log10(P1)) 62 | 63 | r = lowPassFilter12_5G(r); 64 | 65 | figure; 66 | plot(t(index), r(index)) 67 | 68 | X = r; 69 | L = length(X); 70 | Y = fft(X); 71 | P2 = abs(Y/L); 72 | P1 = P2(1:L/2+1); 73 | P1(2:end-1) = 2*P1(2:end-1); 74 | Fs = SampleRate; 75 | f = Fs*(0:(L/2))/L; 76 | figure; 77 | plot(f,20 * log10(P1)) 78 | 79 | r = (r - mean(r)) / std(r); 80 | 81 | ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', 48, 'YLimits', [min(r(100000: 200000)), max(r(100000: 200000))]); 82 | step(ed, r(100000: 200000)); 83 | 84 | tic 85 | % OverSamplingRatio = 1; 86 | OriginalSignal = importdata('.\Original Data\Original_Data.txt'); 87 | OriginalSignal = (OriginalSignal - 0.5 ) * 2; 88 | OriginalData_port1 = OriginalSignal; 89 | shiftnum = 58; 90 | OriginalData_port2 = [-(OriginalSignal(end - shiftnum + 1 : end)); 91 | -(OriginalSignal(1 : end - shiftnum))]; 92 | OriginalData = OriginalData_port1 + 2 * OriginalData_port2; 93 | % OriginalData = OriginalData_port1; 94 | CorrelationResult = zeros(length(r) - OverSamplingRatio * length(OriginalData) + 1, 1); 95 | parfor i = 1 : length(CorrelationResult) 96 | CorrelationResult(i) = sum(r(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 97 | end 98 | figure; 99 | plot(CorrelationResult) 100 | % TODO: Remove tic/toc 101 | toc 102 | 103 | [a, index] = max(CorrelationResult); 104 | ExtractedSignal = r(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 105 | [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 106 | fprintf('The signal error before equalization\n'); 107 | fprintf('Bit error num: %d\n', BitErrorNum); 108 | fprintf('SER: %e\n', SymErrorRate); 109 | fprintf('BER: %e\n', BitErrorRate); 110 | 111 | % linear FFE 112 | 113 | % ChannelLen = 101:10:501; 114 | % alpha = [0.01; 0.003; 0.001]; 115 | % BER = zeros(length(ChannelLen), length(alpha)); 116 | % BitError = zeros(length(ChannelLen), length(alpha)); 117 | % for i = 1 : length(ChannelLen) 118 | % for j = 1 : length(alpha) 119 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen(i), alpha(j), 5); 120 | % % figure; 121 | % % plot(costs); 122 | % % title('Curve of Convergence'); 123 | % % xlabel('Epoch'); ylabel('Cost'); 124 | % 125 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 126 | % fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen(i), alpha(j)); 127 | % fprintf('Bit error num: %d\n', BitErrorNum); 128 | % fprintf('SER: %e\n', SymErrorRate); 129 | % fprintf('BER: %e\n', BitErrorRate); 130 | % BitError(i, j) = BitErrorNum; 131 | % BER(i, j) = BitErrorRate; 132 | % end 133 | % end 134 | % meshz(log10(alpha), ChannelLen, -log10(BER)); 135 | % title('BER vs ChannelLen & alpha') 136 | % xlabel('log10(alpha)') % x-axis label 137 | % ylabel('ChannelLen') % y-axis label 138 | % zlabel('-log10(BER)') % y-axis label 139 | 140 | % tic 141 | % 142 | % ChanLen1st = 301; 143 | % ChanLen2nd = 33; 144 | % ChanLen3rd = 11; 145 | % alpha = 0.003; 146 | % 147 | % [EqualizedSignal, w, costs] = volterraFFEqualize(ExtractedSignal, OriginalData, 'lms', 10, ChanLen1st, alpha, ChanLen2nd, [], ChanLen3rd); 148 | % figure; 149 | % plot(costs); 150 | % title('Curve of Convergence'); 151 | % xlabel('Epoch'); ylabel('Cost'); 152 | % 153 | % EqualizedSignalUS = resample(EqualizedSignal, 48, 1); 154 | % ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', 48, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 155 | % step(ed1, EqualizedSignalUS); 156 | % 157 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 158 | % fprintf('Equalization Setup: Volterra LMS Channel Length Setup is [%d %d %d], alpha is %f\n', ChanLen1st, ChanLen2nd, ChanLen3rd, alpha); 159 | % fprintf('Bit error num: %d\n', BitErrorNum); 160 | % fprintf('SER: %e\n', SymErrorRate); 161 | % fprintf('BER: %e\n', BitErrorRate); 162 | % toc 163 | -------------------------------------------------------------------------------- /Script_RoF_Wireless_CoherentDetection.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | RawSignal = importdata('.\Sampled Data\RoF\wireless\40km\20170731_LO_Changed\C3rof 3dBm carrier00000.dat'); 9 | 10 | SampleRate = 600e9; 11 | OSCRate = 120e9; 12 | DataRate = 12.5e9; 13 | OverSamplingRatio = SampleRate / DataRate; 14 | 15 | SampledSignal = (RawSignal(:, 1) - mean(RawSignal(:, 1))) / std(RawSignal(:, 1)); 16 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 17 | 18 | t = (0:length(SampledSignal)-1)*(1/SampleRate); 19 | t = t'; 20 | 21 | index = find( (t < 22e-9) & (t > 2e-9)); 22 | 23 | fc = 25e9; 24 | lo = cos(2*pi*fc*t) + i * -sin(2*pi*fc*t); 25 | 26 | figure; 27 | % plot(t(index), SampledSignal(index), t(index), real(lo(index)), 'r') 28 | plot(t(index), SampledSignal(index)) 29 | X = SampledSignal; 30 | L = length(X); 31 | Y = fft(X); 32 | P2 = abs(Y/L); 33 | P1 = P2(1:L/2+1); 34 | P1(2:end-1) = 2*P1(2:end-1); 35 | Fs = SampleRate; 36 | f = Fs*(0:(L/2))/L; 37 | figure; 38 | plot(f,20 * log10(P1)) 39 | 40 | r = bandPassFilter12G38G(SampledSignal); 41 | 42 | figure; 43 | plot(t(index), r(index)) 44 | 45 | X = r; 46 | L = length(X); 47 | Y = fft(X); 48 | P2 = abs(Y/L); 49 | P1 = P2(1:L/2+1); 50 | P1(2:end-1) = 2*P1(2:end-1); 51 | Fs = SampleRate; 52 | f = Fs*(0:(L/2))/L; 53 | figure; 54 | plot(f,20 * log10(P1)) 55 | 56 | r = r .* lo; 57 | 58 | X = real(r); 59 | L = length(X); 60 | Y = fft(X); 61 | P2 = abs(Y/L); 62 | P1 = P2(1:L/2+1); 63 | P1(2:end-1) = 2*P1(2:end-1); 64 | Fs = SampleRate; 65 | f = Fs*(0:(L/2))/L; 66 | figure; 67 | plot(f,20 * log10(P1)) 68 | 69 | r = lowPassFilter12_5G(r); 70 | 71 | figure; 72 | subplot(2,1,1); 73 | plot(t(index), real(r(index))) 74 | subplot(2,1,2); 75 | plot(t(index), imag(r(index))) 76 | 77 | X = real(r); 78 | L = length(X); 79 | Y = fft(X); 80 | P2 = abs(Y/L); 81 | P1 = P2(1:L/2+1); 82 | P1(2:end-1) = 2*P1(2:end-1); 83 | Fs = SampleRate; 84 | f = Fs*(0:(L/2))/L; 85 | figure; 86 | plot(f,20 * log10(P1)) 87 | 88 | scatterplot(r) 89 | 90 | r = abs(r); 91 | r = (r - mean(r)) / std(r); 92 | 93 | r = r(501:end); 94 | X = r; 95 | L = length(X); 96 | Y = fft(X); 97 | P2 = abs(Y/L); 98 | P1 = P2(1:L/2+1); 99 | P1(2:end-1) = 2*P1(2:end-1); 100 | Fs = SampleRate; 101 | f = Fs*(0:(L/2))/L; 102 | figure; 103 | plot(f,20 * log10(P1)) 104 | 105 | ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', 48, 'YLimits', [min(r(100000: 200000)), max(r(100000: 200000))]); 106 | step(ed, r(100000: 200000)); 107 | 108 | tic 109 | % OverSamplingRatio = 1; 110 | OriginalSignal = importdata('.\Original Data\Original_Data.txt'); 111 | OriginalSignal = (OriginalSignal - 0.5 ) * 2; 112 | OriginalData_port1 = OriginalSignal; 113 | shiftnum = 58; 114 | OriginalData_port2 = [-(OriginalSignal(end - shiftnum + 1 : end)); 115 | -(OriginalSignal(1 : end - shiftnum))]; 116 | OriginalData = OriginalData_port1 + 2 * OriginalData_port2; 117 | % OriginalData = OriginalData_port1; 118 | CorrelationResult = zeros(length(r) - OverSamplingRatio * length(OriginalData) + 1, 1); 119 | parfor i = 1 : length(CorrelationResult) 120 | CorrelationResult(i) = sum(r(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 121 | end 122 | figure; 123 | plot(CorrelationResult) 124 | % TODO: Remove tic/toc 125 | toc 126 | % 127 | % [a, index] = max(CorrelationResult); 128 | % ExtractedSignal = r(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 129 | % [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 130 | % fprintf('The signal error before equalization\n'); 131 | % fprintf('Bit error num: %d\n', BitErrorNum); 132 | % fprintf('SER: %e\n', SymErrorRate); 133 | % fprintf('BER: %e\n', BitErrorRate); 134 | 135 | % linear FFE 136 | % 137 | % ChannelLen = 101:10:501; 138 | % alpha = [0.001; 0.0003; 0.0001]; 139 | % BER = zeros(length(ChannelLen), length(alpha)); 140 | % BitError = zeros(length(ChannelLen), length(alpha)); 141 | % for i = 1 : length(ChannelLen) 142 | % for j = 1 : length(alpha) 143 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen(i), alpha(j), 5); 144 | % % figure; 145 | % % plot(costs); 146 | % % title('Curve of Convergence'); 147 | % % xlabel('Epoch'); ylabel('Cost'); 148 | % 149 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 150 | % fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen(i), alpha(j)); 151 | % fprintf('Bit error num: %d\n', BitErrorNum); 152 | % fprintf('SER: %e\n', SymErrorRate); 153 | % fprintf('BER: %e\n', BitErrorRate); 154 | % BitError(i, j) = BitErrorNum; 155 | % BER(i, j) = BitErrorRate; 156 | % end 157 | % end 158 | % meshz(log10(alpha), ChannelLen, -log10(BER)); 159 | % title('BER vs ChannelLen & alpha') 160 | % xlabel('log10(alpha)') % x-axis label 161 | % ylabel('ChannelLen') % y-axis label 162 | % zlabel('-log10(BER)') % y-axis label 163 | 164 | tic 165 | 166 | ChanLen1st = 301; 167 | ChanLen2nd = 33; 168 | ChanLen3rd = 11; 169 | alpha = 0.003; 170 | 171 | [EqualizedSignal, w, costs] = volterraFFEqualize(ExtractedSignal, OriginalData, 'lms', 10, ChanLen1st, alpha, ChanLen2nd, [], ChanLen3rd); 172 | figure; 173 | plot(costs); 174 | title('Curve of Convergence'); 175 | xlabel('Epoch'); ylabel('Cost'); 176 | 177 | EqualizedSignalUS = resample(EqualizedSignal, 48, 1); 178 | ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', 48, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 179 | step(ed1, EqualizedSignalUS); 180 | 181 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 182 | fprintf('Equalization Setup: Volterra LMS Channel Length Setup is [%d %d %d], alpha is %f\n', ChanLen1st, ChanLen2nd, ChanLen3rd, alpha); 183 | fprintf('Bit error num: %d\n', BitErrorNum); 184 | fprintf('SER: %e\n', SymErrorRate); 185 | fprintf('BER: %e\n', BitErrorRate); 186 | toc 187 | -------------------------------------------------------------------------------- /Script_RoFWired.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | %% Import sampled data from DSO 9 | % Defining parameters 10 | SampleRate = 200e9; 11 | OSCRate = 40e9; 12 | DataRate = 12.5e9; 13 | OverSamplingRatio = SampleRate / DataRate; 14 | 15 | SampledSignal = importdata('.\Sampled Data\RoF\wired\20170802\C1-1800000.dat'); 16 | SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 17 | SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 18 | 19 | ed = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(SampledSignal), max(SampledSignal)]); 20 | step(ed, SampledSignal); 21 | 22 | tic 23 | % OverSamplingRatio = 1; 24 | OriginalSignal = importdata('.\Original Data\Original_Data.txt'); 25 | OriginalSignal = (OriginalSignal - 0.5 ) * 2; 26 | OriginalData_port1 = OriginalSignal; 27 | shiftnum = 58; 28 | % OriginalData_port2 = [-(OriginalSignal(end - shiftnum + 1 : end)); 29 | % -(OriginalSignal(1 : end - shiftnum))]; 30 | OriginalData_port2 = [-(OriginalSignal(shiftnum + 1 : end)); 31 | -(OriginalSignal(1 : shiftnum))]; 32 | OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 33 | % OriginalData = OriginalData_port1; 34 | CorrelationResult = zeros(length(SampledSignal) - OverSamplingRatio * length(OriginalData) + 1, 1); 35 | parfor i = 1 : length(CorrelationResult) 36 | CorrelationResult(i) = sum(SampledSignal(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 37 | end 38 | figure; 39 | plot(CorrelationResult) 40 | % TODO: Remove tic/toc 41 | toc 42 | 43 | [a, index] = max(CorrelationResult); 44 | ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 45 | [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 46 | fprintf('The signal error before equalization\n'); 47 | fprintf('Bit error num: %d\n', BitErrorNum); 48 | fprintf('SER: %e\n', SymErrorRate); 49 | fprintf('BER: %e\n', BitErrorRate); 50 | 51 | [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', 21, 1e-3, 10); 52 | figure; 53 | plot(costs); 54 | title('Curve of Convergence'); 55 | xlabel('Epoch'); ylabel('Cost'); 56 | 57 | EqualizedSignalUS = resample(EqualizedSignal, OverSamplingRatio, 1); 58 | ed1 = comm.EyeDiagram('DisplayMode','2D color histogram','OversamplingMethod','Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(EqualizedSignalUS), max(EqualizedSignalUS)]); 59 | step(ed1, EqualizedSignalUS); 60 | 61 | [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 62 | fprintf('Equalization Setup: Linear LMS Channel Length is 21, alpha is 1e-3\n'); 63 | fprintf('Bit error num: %d\n', BitErrorNum); 64 | fprintf('SER: %e\n', SymErrorRate); 65 | fprintf('BER: %e\n', BitErrorRate); 66 | 67 | % linear FFE 68 | % ChannelLen = 11:10:201; 69 | % alpha = [0.003; 0.001; 0.0003]; 70 | % BER = zeros(length(ChannelLen), length(alpha)); 71 | % BitError = zeros(length(ChannelLen), length(alpha)); 72 | % for i = 1 : length(ChannelLen) 73 | % for j = 1 : length(alpha) 74 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', ChannelLen(i), alpha(j), 10); 75 | % % figure; 76 | % % plot(costs); 77 | % % title('Curve of Convergence'); 78 | % % xlabel('Epoch'); ylabel('Cost'); 79 | % 80 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 81 | % fprintf('Equalization Setup: Linear LMS Channel Length is %d, alpha is %f\n', ChannelLen(i), alpha(j)); 82 | % fprintf('Bit error num: %d\n', BitErrorNum); 83 | % fprintf('SER: %e\n', SymErrorRate); 84 | % fprintf('BER: %e\n', BitErrorRate); 85 | % BitError(i, j) = BitErrorNum; 86 | % BER(i, j) = BitErrorRate; 87 | % end 88 | % end 89 | % meshz(log10(alpha), ChannelLen, -log10(BER)); 90 | % title('BER vs ChannelLen & alpha') 91 | % xlabel('log10(alpha)') % x-axis label 92 | % ylabel('ChannelLen') % y-axis label 93 | % zlabel('-log10(BER)') % y-axis label 94 | 95 | 96 | % PathToSampledDataFolder = '.\Sampled Data\RoF\wired\20170802\'; 97 | % FileList = dir(PathToSampledDataFolder); 98 | % BERwEQ = zeros(length(FileList) - 2, 1); 99 | % BERwoEQ = BERwEQ; 100 | % for i = 1 : length(FileList) - 2 101 | % PathToSampledData = strcat(PathToSampledDataFolder, FileList(i+2).name); 102 | % SampledSignal = importdata(PathToSampledData); 103 | % SampledSignal = (SampledSignal - mean(SampledSignal)) / std(SampledSignal); 104 | % SampledSignal = resample(SampledSignal, SampleRate, OSCRate); 105 | % tic 106 | % OriginalSignal = importdata('.\Original Data\Original_Data.txt'); 107 | % OriginalSignal = (OriginalSignal - 0.5 ) * 2; 108 | % OriginalData_port1 = OriginalSignal; 109 | % shiftnum = 58; 110 | % OriginalData_port2 = [-(OriginalSignal(shiftnum + 1 : end)); 111 | % -(OriginalSignal(1 : shiftnum))]; 112 | % OriginalData = 2 * OriginalData_port1 + OriginalData_port2; 113 | % % OriginalData = OriginalData_port1; 114 | % CorrelationResult = zeros(length(SampledSignal) - OverSamplingRatio * length(OriginalData) + 1, 1); 115 | % parfor i = 1 : length(CorrelationResult) 116 | % CorrelationResult(i) = sum(SampledSignal(i : OverSamplingRatio : i + OverSamplingRatio * length(OriginalData) - 1) .* OriginalData); 117 | % end 118 | % figure; 119 | % plot(CorrelationResult) 120 | % % TODO: Remove tic/toc 121 | % toc 122 | % 123 | % [a, index] = max(CorrelationResult); 124 | % ExtractedSignal = SampledSignal(index : OverSamplingRatio : index + length(OriginalData) * OverSamplingRatio - 1); 125 | % [BitErrorRate, SymErrorRate, BitErrorNum, OutputSignal] = decisionAndCalcBerPAM4(ExtractedSignal, OriginalData); 126 | % fprintf('The signal error before equalization\n'); 127 | % fprintf('Bit error num: %d\n', BitErrorNum); 128 | % fprintf('SER: %e\n', SymErrorRate); 129 | % fprintf('BER: %e\n', BitErrorRate); 130 | % BERwoEQ(i) = BitErrorRate; 131 | % 132 | % [EqualizedSignal, w, costs] = linearFFEqualize(ExtractedSignal, OriginalData, 'lms', 21, 1e-3, 10); 133 | % % figure; 134 | % % plot(costs); 135 | % % title('Curve of Convergence'); 136 | % % xlabel('Epoch'); ylabel('Cost'); 137 | % 138 | % [BitErrorRate, SymErrorRate, BitErrorNum] = decisionAndCalcBerPAM4(EqualizedSignal, OriginalData); 139 | % fprintf('Equalization Setup: Linear LMS Channel Length is 21, alpha is 1e-3\n'); 140 | % fprintf('Bit number num: %d \n', BitErrorNum); 141 | % fprintf('SER: %e\n', SymErrorRate); 142 | % fprintf('BER: %e\n', BitErrorRate); 143 | % BERwEQ(i) = BitErrorRate; 144 | % end 145 | % 146 | % x = [-12; -13; -14; -15; -17; -18; -19; -20; -21; -22]; 147 | % plot(x, log10(BERwEQ), x, log10(BERwoEQ), 'r') 148 | % title('BER curve w(b)/wo(r) FFE'); 149 | % xlabel('ROP/dBm'); ylabel('log10(BER)'); 150 | -------------------------------------------------------------------------------- /Script_PTLSim.m: -------------------------------------------------------------------------------- 1 | clear all; 2 | close all; 3 | clc; 4 | 5 | %% change the current directory to the folder which contains this m file 6 | cd(fileparts(which(mfilename))); 7 | 8 | DataLength = 500000; 9 | SampleRate = 100e9; 10 | SymbolRate = 25e9; 11 | OverSamplingRatio = SampleRate / SymbolRate; 12 | rng('shuffle'); 13 | % Tx signal generation 14 | PAM4Data = randi(4, DataLength, 1) - 1; 15 | TxData = [PAM4Data(end - 99: end); PAM4Data; PAM4Data(1: 100)]; 16 | % TxSignal = resample(TxData, SampleRate, SymbolRate); 17 | TxSignal = reshape(repmat(TxData, 1, OverSamplingRatio)', [], 1); 18 | TxSignal = (TxSignal - mean(TxSignal)) / std(TxSignal); 19 | plotInFreq(TxSignal, SampleRate) 20 | 21 | % Transmit through a band-limited AWGN channel 22 | RxSignal = TxSignal; 23 | % RxSignal = zeros(size(TxSignal)); 24 | % RxSignal = awgn(RxSignal, 22.5); 25 | % RxSignal = lowPass10G(RxSignal); 26 | RxSignal = awgn(RxSignal, 20); 27 | % RxSignal = lowPass30G(RxSignal); 28 | % RxSignal = (RxSignal - mean(RxSignal)) / std(RxSignal); 29 | % RxSignal = lowPass10G(RxSignal); 30 | plotInFreq(RxSignal, SampleRate) 31 | 32 | % % TODO remove this 33 | % ed0 = comm.EyeDiagram('DisplayMode', '2D color histogram', 'OversamplingMethod', 'Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(RxSignal), max(RxSignal)]); 34 | % step(ed0, RxSignal); 35 | 36 | % TODO Matched Filtering 37 | RxSignal = filter(ones(OverSamplingRatio, 1), 1, RxSignal); 38 | plotInFreq(RxSignal, SampleRate) 39 | 40 | % Extracted transmitted signal 41 | tic 42 | OriginalDataUS = upsample(PAM4Data, OverSamplingRatio); 43 | CorrelationResult = conv(RxSignal, OriginalDataUS(end:-1:1), 'valid'); 44 | figure; 45 | plot(CorrelationResult); 46 | toc 47 | [a, index] = max(CorrelationResult); 48 | 49 | % ExtractedSignal = RxSignal(index : index + DataLength * OverSamplingRatio - 1); 50 | % ed = comm.EyeDiagram('DisplayMode', '2D color histogram', 'OversamplingMethod', 'Input interpolation', 'SamplesPerSymbol', OverSamplingRatio, 'YLimits', [min(ExtractedSignal), max(ExtractedSignal)]); 51 | % step(ed, ExtractedSignal); 52 | 53 | % ExtractedSignal = RxSignal(index : OverSamplingRatio : index + DataLength * OverSamplingRatio - 1); 54 | % Dataset = zeros(DataLength - 100, 101); 55 | % Noise = awgn(zeros(size(ExtractedSignal)), 25); 56 | % NoiseDataset = zeros(DataLength - 100, 101);; 57 | % for i = 1 : length(Dataset) 58 | % Dataset(i, :) = ExtractedSignal(i : i + 100, :)'; 59 | % NoiseDataset(i, :) = Noise(i : i + 100, :)'; 60 | % end 61 | % figure; 62 | % subplot(2, 1, 1); plot(svd(Dataset)) 63 | % subplot(2, 1, 2); plot(svd(NoiseDataset)) 64 | 65 | % x = zeros(length(ExtractedSignal) - 101 + 1, 101); 66 | % for i = 1 : length(ExtractedSignal) - 101 + 1 67 | % x(i, :) = ExtractedSignal(i : i + 101 - 1)'; 68 | % end 69 | % Sigma = (1 / size(x, 1)) * x' * x; 70 | % [U, S, V] = svd(Sigma); 71 | % k = 70; 72 | % z = x * U(:, 1 : k); 73 | % x_approx = z * U(:, 1 : k)'; 74 | % figure;plot(sum(S)); 75 | % sum(sum((x - x_approx) .^ 2)) / sum(sum((x) .^ 2)) 76 | csvwrite(['.\Sampled Data\Simulation\', 'rx.csv'], ExtractedSignal); 77 | csvwrite(['.\Sampled Data\Simulation\', 'tx.csv'], PAM4Data); 78 | 79 | %% Aux Functions 80 | function y = plotInFreq(in, Fs) 81 | % plotInFreq: plot signal in frequency domain 82 | L = length(in); 83 | Y = fft(in); 84 | M2 = abs(Y / L); 85 | M1 = M2(1 : L/2 + 1); 86 | M1(2 : end - 1) = 2 * M1(2 : end - 1); 87 | P2 = angle(Y / L); 88 | P1 = P2(1 : L/2 + 1) / pi; 89 | f = Fs * (0 : (L / 2)) / L; 90 | 91 | figure; 92 | % subplot(2, 1, 1); 93 | plot(f, 20 * log10(M1)) 94 | % subplot(2, 1, 2); 95 | % plot(f, P1) 96 | end 97 | 98 | function y = lowPass10G(x) 99 | % % 20-order 10G Kaiser LPF 100 | % % All frequency values are in GHz. 101 | % Fs = 100; % Sampling Frequency 102 | % N = 20; % Order 103 | % Fc = 10; % Cutoff Frequency 104 | % flag = 'scale'; % Sampling Flag 105 | % Beta = 1; % Window Parameter 106 | Hd = dsp.FIRFilter( ... 107 | 'Numerator', [-6.64318883454626e-18 -0.01856525816569 ... 108 | -0.0352121682532305 -0.041700902608702 -0.0309951315585328 ... 109 | 7.94840490930163e-18 0.0486756433241017 0.106687218997798 ... 110 | 0.161838954883738 0.201391397662279 0.215760491436478 0.201391397662279 ... 111 | 0.161838954883738 0.106687218997798 0.0486756433241017 ... 112 | 7.94840490930163e-18 -0.0309951315585328 -0.041700902608702 ... 113 | -0.0352121682532305 -0.01856525816569 -6.64318883454626e-18]); 114 | y = step(Hd, double(x)); 115 | 116 | % % A butterworth LPF simulating DML/PD inband frequency fading 117 | % Fpass = 1; % Passband Frequency 118 | % Fstop = 20; % Stopband Frequency 119 | % Apass = 1; % Passband Ripple (dB) 120 | % Astop = 10; % Stopband Attenuation (dB) 121 | % Fs = 100; % Sampling Frequency 122 | % h = fdesign.lowpass('fp,fst,ap,ast', Fpass, Fstop, Apass, Astop, Fs); 123 | % Hd = design(h, 'butter', ... 124 | % 'MatchExactly', 'stopband', ... 125 | % 'SystemObject', true); 126 | % x = step(Hd, double(x)); 127 | % % 4th-order Bessel LPF 128 | % % All frequency values are in GHz. 129 | % Nb = 4; % Numerator Order 130 | % Na = 4; % Denominator Order 131 | % F3dB = 12; % 3-dB Frequency 132 | % Fs = 100; % Sampling Frequency 133 | % h = fdesign.lowpass('nb,na,f3db', Nb, Na, F3dB, Fs); 134 | % Hd = design(h, 'butter', 'SystemObject', true); 135 | % y = step(Hd, double(x)); 136 | end 137 | 138 | function y = lowPass25G(x) 139 | % % All frequency values are in GHz. 140 | % Fs = 100; % Sampling Frequency 141 | % N = 20; % Order 142 | % Fc = 25; % Cutoff Frequency 143 | % flag = 'scale'; % Sampling Flag 144 | % Beta = 1; % Window Parameter 145 | Hd = dsp.FIRFilter( ... 146 | 'Numerator', [1.50218896096586e-17 0.0285686861166202 ... 147 | -1.64045849115145e-17 -0.0396594867673816 1.75232583993696e-17 ... 148 | 0.0587052701799814 -1.83460182902814e-17 -0.101464478833822 ... 149 | 1.88493140942653e-17 0.309906147011409 0.487887724586386 ... 150 | 0.309906147011409 1.88493140942653e-17 -0.101464478833822 ... 151 | -1.83460182902814e-17 0.0587052701799814 1.75232583993696e-17 ... 152 | -0.0396594867673816 -1.64045849115145e-17 0.0285686861166202 ... 153 | 1.50218896096586e-17]); 154 | y = step(Hd,double(x)); 155 | end 156 | 157 | function y = lowPass5G(x) 158 | % % All frequency values are in GHz. 159 | % Fs = 100; % Sampling Frequency 160 | % N = 20; % Order 161 | % Fc = 5; % Cutoff Frequency 162 | % flag = 'scale'; % Sampling Flag 163 | % Beta = 1; % Window Parameter 164 | Hd = dsp.FIRFilter( ... 165 | 'Numerator', [2.71637240797039e-18 0.00798192017345794 ... 166 | 0.017797042461414 0.0290094491915146 0.0410132167097605 ... 167 | 0.0530776687430912 0.0644083314945248 0.0742175172547654 ... 168 | 0.0817971427167792 0.0865859254643814 0.0882235715806219 ... 169 | 0.0865859254643814 0.0817971427167792 0.0742175172547654 ... 170 | 0.0644083314945248 0.0530776687430912 0.0410132167097605 ... 171 | 0.0290094491915146 0.017797042461414 0.00798192017345794 ... 172 | 2.71637240797039e-18]); 173 | y = step(Hd,double(x)); 174 | end 175 | 176 | function y = lowPass30G(x) 177 | Fpass = 30; % Passband Frequency 178 | Fstop = 33; % Stopband Frequency 179 | Apass = 1; % Passband Ripple (dB) 180 | Astop = 80; % Stopband Attenuation (dB) 181 | Fs = 100; % Sampling Frequency 182 | h = fdesign.lowpass('fp,fst,ap,ast', Fpass, Fstop, Apass, Astop, Fs); 183 | Hd = design(h, 'butter', ... 184 | 'MatchExactly', 'stopband', ... 185 | 'SystemObject', true); 186 | y = step(Hd, double(x)); 187 | end 188 | -------------------------------------------------------------------------------- /volterraFFEqualize.m: -------------------------------------------------------------------------------- 1 | function [output, w, costs] = volterraFFEqualize(InputSignal, TrainingSignal, ... 2 | AlgType, epoch, ... 3 | ChanLen1st, Alpha1st, ... 4 | ChanLen2nd, Alpha2nd, ... 5 | ChanLen3rd, Alpha3rd, ... 6 | EnRAE, WeightNum) 7 | % This function performs the volterra feedforward equalization with LMS or RLS algorithm. 8 | % For now only 1st-3rd order are supported. 1st order must be included, while 2nd 9 | % and 3rd orders are optional and controlled by the ChanLen2nd and ChanLen3rd 10 | % flags. The LMS learning rate of different order can be different by adjusting the 11 | % Alpha1st, Alpha2nd and Alpha3rd. The equalizer will be trained on the 12 | % InputSignal epoch times and will then perform a equalization. 13 | % 14 | % input: 15 | % InputSignal 16 | % The input signal to be equalized. 17 | % TrainingSignal 18 | % The actual signal to be equalized to. 19 | % AlgType 20 | % 'lms' for LMS or 'rls' for RLS. 21 | % epoch (optional) 22 | % The epoch of the learning through all the input signal. 23 | % Default: 1 24 | % ChanLen1st (optional) 25 | % The channel length of 1st order kernel. 26 | % Default: 5 27 | % Alpha1st (optional) 28 | % The 1st order kernel learning rate of LMS algorithm or the forgetting factor of RLS. 29 | % Default: 0.01 for AlgType = 'lms', 0.99 for AlgType = 'rls' 30 | % ChanLen2nd (optional) 31 | % The channel length of 2nd order kernel. 32 | % Default: ChanLen1st 33 | % Alpha2nd (optional) 34 | % The 2nd order kernel learning rate of LMS algorithm or the forgetting factor of RLS. 35 | % In rls algorithm, this parameter is forced to be Alpha1st. 36 | % Default: Alpha1st 37 | % ChanLen3rd (optional) 38 | % The channel length of 3rd order kernel. 39 | % Default: ChanLen1st 40 | % Alpha3rd (optional) 41 | % The 3rd order kernel learning rate of LMS algorithm or the forgetting factor of RLS. 42 | % In rls algorithm, this parameter is forced to be Alpha1st. 43 | % Default: Alpha1st 44 | % EnRAE (optional) 45 | % The flag of enable RAE search algorithm. 46 | % Default: false 47 | % WeightNum (optional) 48 | % The weight number to be simplified to by RAE. 49 | % Default: 100 50 | % 51 | % output: 52 | % output 53 | % The equalized signal with the same length of InputSignal 54 | % Size: length(InputSignal), 1 55 | % w 56 | % Weights of volterra series 57 | % Size: length(Q, 2), 1 58 | % costs 59 | % The costs after each training epoch, which is used to draw a 60 | % curve of convergence and thus determine the best learning rate. 61 | 62 | %% Paramete_raer Checking 63 | narginchk(3, 12); 64 | 65 | if ~exist('epoch','var') || isempty(epoch) 66 | epoch = 1; 67 | end 68 | 69 | if ~exist('ChanLen1st','var') || isempty(ChanLen1st) 70 | ChanLen1st = 5; 71 | end 72 | if ~exist('Alpha1st','var') || isempty(Alpha1st) 73 | if strcmp(AlgType, 'lms') 74 | Alpha1st = 0.01; 75 | elseif strcmp(AlgType, 'rls') 76 | Alpha1st = 0.99; 77 | end 78 | end 79 | 80 | if ~exist('ChanLen2nd','var') || isempty(ChanLen2nd) 81 | ChanLen2nd = ChanLen1st; 82 | end 83 | 84 | if ~exist('Alpha2nd','var') || isempty(Alpha2nd) 85 | Alpha2nd = Alpha1st; 86 | end 87 | 88 | if ~exist('ChanLen3rd','var') || isempty(ChanLen3rd) 89 | ChanLen3rd = ChanLen1st; 90 | end 91 | 92 | if ~exist('Alpha3rd','var') || isempty(Alpha3rd) 93 | Alpha3rd = Alpha1st; 94 | end 95 | 96 | if ~exist('EnRAE','var') || isempty(EnRAE) 97 | EnRAE = false; 98 | end 99 | 100 | if ~exist('WeightNum','var') || isempty(WeightNum) 101 | WeightNum = 150; 102 | end 103 | 104 | % TODO add some parameter checking 105 | if ChanLen1st <= 0 106 | error('volterraFeedForwardEqualize:argChk', '1st order Channel length must be greater than 0'); 107 | end 108 | 109 | if mod(ChanLen1st, 2) == 0 110 | error('volterraFeedForwardEqualize:argChk', 'Channel length must be odd'); 111 | end 112 | 113 | if (ChanLen2nd ~= 0) && (ChanLen3rd ~= 0) 114 | if (mod(ChanLen2nd, 2) == 0) || (mod(ChanLen3rd, 2) == 0) 115 | error('volterraFeedForwardEqualize:argChk', 'Channel length must be odd'); 116 | end 117 | end 118 | 119 | if ~strcmp(AlgType, 'lms') && ~strcmp(AlgType, 'rls') 120 | error('volterraFeedForwardEqualize:argChk', 'AlgType must be lms or rls'); 121 | end 122 | 123 | if ChanLen1st <= 0 124 | error('volterraFeedForwardEqualize:argChk', '1st order channel length must be bigger than 0'); 125 | end 126 | 127 | %% Signal Normalization and Duplication 128 | % InputSignal and TrainingSignal is normalized to the range between 0-1. 129 | InputSignal = InputSignal - min(InputSignal); 130 | InputSignal = InputSignal / max(InputSignal); 131 | TrainingSignal = TrainingSignal - min(TrainingSignal); 132 | TrainingSignal = TrainingSignal / max(TrainingSignal); 133 | 134 | % Both signal is duplicated for better performance 135 | InputSignalDup = repmat(InputSignal, 2, 1); 136 | TrainingSignalDup = repmat(TrainingSignal, 2, 1); 137 | % Zero Padding for input signal 138 | MaxChanLen = max([ChanLen1st, ChanLen2nd, ChanLen3rd]); 139 | InputSignalZP = [InputSignalDup(end-floor(MaxChanLen/2)+1 : end); InputSignalDup; InputSignalDup(1:floor(MaxChanLen/2))]; 140 | 141 | %% Calculating the kernel size 142 | KernelSize = ChanLen1st; 143 | Kernel2ndSize = 0; 144 | Kernel3rdSize = 0; 145 | if ChanLen2nd ~= 0 146 | for k = 1 : ChanLen2nd 147 | for m = k : ChanLen2nd 148 | Kernel2ndSize = Kernel2ndSize + 1; 149 | end 150 | end 151 | KernelSize = KernelSize + Kernel2ndSize; 152 | end 153 | if ChanLen3rd ~= 0 154 | for k = 1 : ChanLen3rd 155 | for m = k : ChanLen3rd 156 | for n = m : ChanLen3rd 157 | Kernel3rdSize = Kernel3rdSize + 1; 158 | end 159 | end 160 | end 161 | KernelSize = KernelSize + Kernel3rdSize; 162 | end 163 | 164 | %% Weights Searching using RAE 165 | % Generating input matrix 166 | X = zeros(length(InputSignalDup), KernelSize); 167 | for i = 1 : ChanLen1st 168 | X(:, i) = InputSignalZP(floor(MaxChanLen/2) + i - floor(ChanLen1st/2) : ... 169 | floor(MaxChanLen/2) + i - floor(ChanLen1st/2) + length(InputSignalDup) - 1); 170 | end 171 | t = ChanLen1st; 172 | if ChanLen2nd ~= 0 173 | for k = 1 : ChanLen2nd 174 | for m = k : ChanLen2nd 175 | t = t + 1; 176 | X(:, t) = InputSignalZP(floor(MaxChanLen/2) + k - floor(ChanLen2nd/2) : ... 177 | floor(MaxChanLen/2) + k - floor(ChanLen2nd/2) + length(InputSignalDup) - 1) .*... 178 | InputSignalZP(floor(MaxChanLen/2) + m - floor(ChanLen2nd/2) : ... 179 | floor(MaxChanLen/2) + m - floor(ChanLen2nd/2) + length(InputSignalDup) - 1); 180 | end 181 | end 182 | end 183 | if ChanLen3rd ~= 0 184 | for k = 1 : ChanLen3rd 185 | for m = k : ChanLen3rd 186 | for n = m : ChanLen3rd 187 | t = t + 1; 188 | X(:, t) = InputSignalZP(floor(MaxChanLen/2) + k - floor(ChanLen3rd/2) : ... 189 | floor(MaxChanLen/2) + k - floor(ChanLen3rd/2) + length(InputSignalDup) - 1) .*... 190 | InputSignalZP(floor(MaxChanLen/2) + m - floor(ChanLen3rd/2) : ... 191 | floor(MaxChanLen/2) + m - floor(ChanLen3rd/2) + length(InputSignalDup) - 1) .*... 192 | InputSignalZP(floor(MaxChanLen/2) + n - floor(ChanLen3rd/2) : ... 193 | floor(MaxChanLen/2) + n - floor(ChanLen3rd/2) + length(InputSignalDup) - 1); 194 | end 195 | end 196 | end 197 | end 198 | 199 | % RAE process: Q is the output selected kernel matrix 200 | if EnRAE == true 201 | y = TrainingSignalDup; 202 | p = 1; 203 | ete_rae = zeros(KernelSize, KernelSize); 204 | 205 | for i = 1 : KernelSize 206 | w_rae = (X(:, i)' * y) / (X(:, i)' * X(:, i)); 207 | ete_rae(1, i) = y' * y - w_rae * X(:, i)' * y; 208 | end 209 | [ete_rae_min, i] = min(ete_rae(1, :)); 210 | w_rae = (X(:, i)' * y) / (X(:, i)' * X(:, i)); 211 | Q = [X(:, i)]; 212 | if i <= ChanLen1st 213 | KernelOrder = [1]; 214 | elseif i <= ChanLen1st + Kernel2ndSize 215 | KernelOrder = [2]; 216 | else 217 | KernelOrder = [3]; 218 | end 219 | % find the center position 220 | if i == floor(ChanLen1st / 2) + 1 221 | CenterPosition = size(Q, 2); 222 | end 223 | R = Q' / (Q' * Q); 224 | p = p + 1; 225 | 226 | while (p <= WeightNum) 227 | for i = 1 : KernelSize 228 | if ismember(X(:, i)', Q', 'rows') == 1 229 | ete_rae(p, i) = inf; 230 | continue 231 | end 232 | z_rae = R * X(:, i); 233 | delta_rae = 1 / (X(:, i)' * X(:, i) - X(:, i)' * Q * z_rae); 234 | g_rae = delta_rae * (z_rae' * Q' - X(:, i)'); 235 | w_rae_temp = [w_rae + z_rae * g_rae * y; -g_rae * y]; 236 | ete_rae(p, i) = y' * y - y' * [Q, X(:, i)] * w_rae_temp; 237 | end 238 | [ete_rae_min, i] = min(ete_rae(p, :)); 239 | z_rae = R * X(:, i); 240 | delta_rae = 1 / (X(:, i)' * X(:, i) - X(:, i)' * Q * z_rae); 241 | g_rae = delta_rae * (z_rae' * Q' - X(:, i)'); 242 | w_rae = [w_rae + z_rae * g_rae * y; -g_rae * y]; 243 | Q = [Q, X(:, i)]; 244 | if i <= ChanLen1st 245 | KernelOrder = [KernelOrder; 1]; 246 | elseif i <= ChanLen1st + Kernel2ndSize 247 | KernelOrder = [KernelOrder; 2]; 248 | else 249 | KernelOrder = [KernelOrder; 3]; 250 | end 251 | % find the center position 252 | if i == floor(ChanLen1st / 2) + 1 253 | CenterPosition = size(Q, 2); 254 | end 255 | R = [R + z_rae * g_rae; -g_rae]; 256 | p = p + 1; 257 | end 258 | else 259 | Q = X; 260 | KernelOrder = [zeros(ChanLen1st, 1) + 1; 261 | zeros(Kernel2ndSize, 1) + 2; 262 | zeros(Kernel3rdSize, 1) + 3;]; 263 | CenterPosition = floor(ChanLen1st / 2) + 1; 264 | end 265 | % Define and randomly init weights vector to (-1, 1) 266 | rng('shuffle'); 267 | w = zeros(size(Q, 2), 1); 268 | w(CenterPosition) = 1; 269 | 270 | %% Training epoch times 271 | costs = zeros(epoch, 1); 272 | y = zeros(size(TrainingSignalDup)); 273 | if strcmp(AlgType, 'lms') 274 | % The LMS learning algorithm 275 | for iter = 1 : epoch 276 | for i = 1 : size(Q, 1) 277 | y(i) = Q(i, :) * w; 278 | % Construct a diagnose matrix for different learning rate of different order 279 | alpha = [Alpha1st; Alpha2nd; Alpha3rd]; 280 | alpha = diag(alpha(KernelOrder)); 281 | % learning step 282 | w = w - alpha * (y(i) - TrainingSignalDup(i)) * Q(i, :)'; 283 | costs(iter) = costs(iter) + 0.5 * ((y(i) - TrainingSignalDup(i)) ^ 2); 284 | end 285 | % Record the cost/error of each epoch 286 | costs(iter) = costs(iter) / size(Q, 1); 287 | end 288 | elseif strcmp(AlgType, 'rls') 289 | % The RLS learning algorithm 290 | Sd = eye(size(Q, 2)); 291 | for iter = 1 : epoch 292 | for i = 1 : size(Q, 1) 293 | x = Q(i, :)'; 294 | e = TrainingSignalDup(i) - w' * x; 295 | phi = Sd * x; 296 | Sd = (1 / Alpha1st) * (Sd - (phi * phi') / (Alpha1st + phi' * x)); 297 | w = w + e * Sd * x; 298 | costs(iter) = costs(iter) + 0.5 * (e ^ 2); 299 | end 300 | % Record the cost/error of each epoch 301 | costs(iter) = costs(iter) / size(Q, 1); 302 | end 303 | end 304 | 305 | %% Using Trained Weights to Equalize Data 306 | for i = 1 : size(Q, 1) 307 | y(i) = Q(i, :) * w; 308 | end 309 | 310 | % TODO choose a half of the output 311 | output = y(1 : length(y) / 2); 312 | % output = y(length(y) / 2 + 1 : end); 313 | -------------------------------------------------------------------------------- /bandPassFilter15G35G.m: -------------------------------------------------------------------------------- 1 | function y = bandPassFilter15G35G(x) 2 | %BANDPASSFILTER15G35G Filters input x and returns output y. 3 | 4 | % MATLAB Code 5 | % Generated by MATLAB(R) 9.1 and the DSP System Toolbox 9.3. 6 | % Generated on: 29-Jul-2017 10:05:38 7 | 8 | %#codegen 9 | 10 | % To generate C/C++ code from this function use the codegen command. Type 11 | % 'help codegen' for more information. 12 | 13 | persistent Hd; 14 | 15 | if isempty(Hd) 16 | 17 | % The following code was used to design the filter coefficients: 18 | % % Equiripple Bandpass filter designed using the FIRPM function. 19 | % 20 | % % All frequency values are in GHz. 21 | % Fs = 600; % Sampling Frequency 22 | % 23 | % Fstop1 = 15; % First Stopband Frequency 24 | % Fpass1 = 16; % First Passband Frequency 25 | % Fpass2 = 34; % Second Passband Frequency 26 | % Fstop2 = 35; % Second Stopband Frequency 27 | % Dstop1 = 0.01; % First Stopband Attenuation 28 | % Dpass = 0.057501127785; % Passband Ripple 29 | % Dstop2 = 0.01; % Second Stopband Attenuation 30 | % dens = 20; % Density Factor 31 | % 32 | % % Calculate the order from the parameters using FIRPMORD. 33 | % [N, Fo, Ao, W] = firpmord([Fstop1 Fpass1 Fpass2 Fstop2]/(Fs/2), [0 1 ... 34 | % 0], [Dstop1 Dpass Dstop2]); 35 | % 36 | % % Calculate the coefficients using the FIRPM function. 37 | % b = firpm(N, Fo, Ao, W, {dens}); 38 | 39 | Hd = dsp.FIRFilter( ... 40 | 'Numerator', [-0.00328662612537043 0.00304400411355789 ... 41 | 0.00100889182085265 -0.000438850868031051 -0.00132696181743952 ... 42 | -0.00175190210005007 -0.00183121533693241 -0.00166979884539573 ... 43 | -0.00135063007552091 -0.000930213854910791 -0.000447793739480768 ... 44 | 7.01502854637626e-05 0.000599082943233155 0.00111499796016897 ... 45 | 0.00158879370188017 0.00199042106393222 0.00228805569576622 ... 46 | 0.00245534154963468 0.00247186695365886 0.00232996454314494 ... 47 | 0.00203343613493045 0.00160156054722416 0.00106464066323764 ... 48 | 0.000464789604579942 -0.000151235860772574 -0.000733304467499819 ... 49 | -0.00123606394057572 -0.00162040071692578 -0.00185954669787317 ... 50 | -0.00194133868297838 -0.00186455281350489 -0.00165290903213645 ... 51 | -0.00132321370172661 -0.000928389644096508 -0.000498549297996215 ... 52 | -8.06686137619136e-05 0.000281683148107482 0.00056663832843261 ... 53 | 0.000752974827932438 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... 301 | -0.000463496237267545 -0.000682850390710184 -0.000874312333930782 ... 302 | -0.00101043550147316 -0.00107158841536745 -0.00104369214945667 ... 303 | -0.000921489111515442 -0.000715518199022344 -0.000439850320229998 ... 304 | -0.000124802121067749 0.000201879978037315 0.00050681707540784 ... 305 | 0.000762991498849653 0.000946025734548229 0.00104141933081865 ... 306 | 0.00104440356130544 0.000963726196764916 0.000817087660524844 ... 307 | 0.000629597193713792 0.000428414596537098 0.000243516189945656 ... 308 | 0.000102114691495288 2.54452473931457e-05 2.15970925174225e-05 ... 309 | 8.86016232063873e-05 0.000214905085461564 0.000381800229405877 ... 310 | 0.000558954402278686 0.000712108483391345 0.000810340132873889 ... 311 | 0.000831017438674074 0.000752974827932438 0.00056663832843261 ... 312 | 0.000281683148107482 -8.06686137619136e-05 -0.000498549297996215 ... 313 | -0.000928389644096508 -0.00132321370172661 -0.00165290903213645 ... 314 | -0.00186455281350489 -0.00194133868297838 -0.00185954669787317 ... 315 | -0.00162040071692578 -0.00123606394057572 -0.000733304467499819 ... 316 | -0.000151235860772574 0.000464789604579942 0.00106464066323764 ... 317 | 0.00160156054722416 0.00203343613493045 0.00232996454314494 ... 318 | 0.00247186695365886 0.00245534154963468 0.00228805569576622 ... 319 | 0.00199042106393222 0.00158879370188017 0.00111499796016897 ... 320 | 0.000599082943233155 7.01502854637626e-05 -0.000447793739480768 ... 321 | -0.000930213854910791 -0.00135063007552091 -0.00166979884539573 ... 322 | -0.00183121533693241 -0.00175190210005007 -0.00132696181743952 ... 323 | -0.000438850868031051 0.00100889182085265 0.00304400411355789 ... 324 | -0.00328662612537043]); 325 | end 326 | 327 | y = step(Hd,double(x)); 328 | 329 | 330 | % [EOF] 331 | -------------------------------------------------------------------------------- /bandPassFilter12G38G.m: -------------------------------------------------------------------------------- 1 | function y = bandPassFilter12G38G(x) 2 | %BANDPASSFILTER12G38G Filters input x and returns output y. 3 | 4 | % MATLAB Code 5 | % Generated by MATLAB(R) 9.1 and the DSP System Toolbox 9.3. 6 | % Generated on: 28-Jul-2017 14:14:10 7 | 8 | %#codegen 9 | 10 | % To generate C/C++ code from this function use the codegen command. Type 11 | % 'help codegen' for more information. 12 | 13 | persistent Hd; 14 | 15 | if isempty(Hd) 16 | 17 | % The following code was used to design the filter coefficients: 18 | % % Equiripple Bandpass filter designed using the FIRPM function. 19 | % 20 | % % All frequency values are in GHz. 21 | % Fs = 600; % Sampling Frequency 22 | % 23 | % Fstop1 = 11; % First Stopband Frequency 24 | % Fpass1 = 12; % First Passband Frequency 25 | % Fpass2 = 38; % Second Passband Frequency 26 | % Fstop2 = 39; % Second Stopband Frequency 27 | % Dstop1 = 0.01; % First Stopband Attenuation 28 | % Dpass = 0.057501127785; % Passband Ripple 29 | % Dstop2 = 0.01; % Second Stopband Attenuation 30 | % dens = 20; % Density Factor 31 | % 32 | % % Calculate the order from the parameters using FIRPMORD. 33 | % [N, Fo, Ao, W] = firpmord([Fstop1 Fpass1 Fpass2 Fstop2]/(Fs/2), [0 1 ... 34 | % 0], [Dstop1 Dpass Dstop2]); 35 | % 36 | % % Calculate the coefficients using the FIRPM function. 37 | % b = firpm(N, Fo, Ao, W, {dens}); 38 | 39 | Hd = dsp.FIRFilter( ... 40 | 'Numerator', [0.00441355600153117 -0.00186851816397141 ... 41 | -0.00157370713114779 -0.00134843843172702 -0.00117137124297013 ... 42 | -0.0010107443034646 -0.000862114418365961 -0.000712726807495988 ... 43 | -0.000577035216303767 -0.000458292846564782 -0.0003816461205007 ... 44 | -0.000353545485975799 -0.000394179580544488 -0.000496899333793406 ... 45 | -0.000662713612794409 -0.000862130982321538 -0.00107386143967476 ... 46 | -0.00124993663037054 -0.00135886579937377 -0.00135240424690182 ... 47 | -0.0012114325405598 -0.000910323983903008 -0.000463578741140334 ... 48 | 0.000117220153030299 0.000779724453430603 0.00147102389173814 ... 49 | 0.00212920622287149 0.0026744932687209 0.00305067423893676 ... 50 | 0.0032172861845596 0.00313705089397488 0.0028145334147178 ... 51 | 0.0022707550011296 0.0015636605122513 0.000752988044242502 ... 52 | -7.67217364537999e-05 -0.000853338810487761 -0.00149634900145798 ... 53 | -0.00195693426079309 -0.00219418161359554 -0.00221004575422702 ... 54 | -0.002017236905325 -0.00166745545718656 -0.00121232311086121 ... 55 | -0.000728040883331772 -0.000272813899063807 8.73201469754458e-05 ... 56 | 0.000321115610522015 0.000399943444625612 0.000336501435816117 ... 57 | 0.000147883148801144 -0.000119583148643723 -0.000418648226511807 ... 58 | -0.000693177160645144 -0.000903766902394608 -0.00100875558385843 ... 59 | -0.00100006055139718 -0.000874321142778759 -0.000658580757554115 ... 60 | -0.000378573620663888 -8.33641642906061e-05 0.000191937587439258 ... 61 | 0.000404014834464624 0.000536706886217158 0.000573536786512098 ... 62 | 0.000528894501482778 0.000416214572303194 0.000275080671860863 ... 63 | 0.000134492374813304 3.73914030709788e-05 2.84290364384147e-06 ... 64 | 5.36711665057605e-05 0.000181057233804151 0.000378473075186087 ... 65 | 0.000609593950013892 0.00084639122956572 0.00104366851800283 ... 66 | 0.00117305271532155 0.00120409027056389 0.00113315427677536 ... 67 | 0.000957920423536317 0.000706959534604148 0.000406087327598929 ... 68 | 0.000101859272868335 -0.000172586318883111 -0.000374723448002457 ... 69 | -0.000488291423316057 -0.000497679147040258 -0.000418096411620716 ... 70 | -0.000264866620382325 -7.97707623321105e-05 0.00010397416415684 ... 71 | 0.000238970209283056 0.000300152076643192 0.000260150732111351 ... 72 | 0.000122642674771688 -0.000107297154159663 -0.000393624800223724 ... 73 | -0.000705418250258672 -0.000990638264621241 -0.00121536741674112 ... 74 | -0.00133949776678719 -0.0013508056561749 -0.00124088094652763 ... 75 | -0.00103218319469495 -0.000747926712404859 -0.000436731328019749 ... 76 | -0.00013640388116154 0.000104535760632245 0.000261396848418661 ... 77 | 0.000310142715254736 0.000259094748665028 0.000119751902365763 ... 78 | -6.78972379290609e-05 -0.000268918761323122 -0.000431513003764218 ... 79 | -0.000523129536161816 -0.000508766312272604 -0.000384112402664683 ... 80 | -0.000149011709285902 0.000164532208657912 0.000524819540649428 ... 81 | 0.000877046137804631 0.00118088263755 0.00138755940286476 ... 82 | 0.00147681808774561 0.00143146564868001 0.00126769686696591 ... 83 | 0.00100649396470428 0.000696166259980961 0.000378366270363941 ... 84 | 0.000108535624328135 -8.04092084326583e-05 -0.000156490767794844 ... 85 | -0.000120040080564369 2.23312416806187e-05 0.00023202693082401 ... 86 | 0.000471402208790895 0.000683507268585274 0.000827404464007775 ... 87 | 0.000858761292792778 0.000763672984825566 0.000535078367846402 ... 88 | 0.000200165612188398 -0.00020948176359988 -0.000636331274167151 ... 89 | -0.00103201615511076 -0.00133848754042219 -0.00152421725956339 ... 90 | -0.00156168788103989 -0.0014593324308138 -0.00123316843216536 ... 91 | -0.00093076792413239 -0.000598125198202694 -0.000297344022495656 ... 92 | -7.13422752938843e-05 3.7207200625178e-05 1.89977204034719e-05 ... 93 | -0.000125910149574876 -0.000362676298173467 -0.000652096781265773 ... 94 | -0.000931643985477384 -0.00115100266157657 -0.00125504889725404 ... 95 | -0.00121775095724189 -0.00102196681450541 -0.000687627978958029 ... 96 | -0.000244194379228427 0.000249075645318178 0.00073656286747284 ... 97 | 0.00115039463631262 0.00144571061974221 0.00158304807726546 ... 98 | 0.00155962639372231 0.00138436775884553 0.00110225166510993 ... 99 | 0.000761812721725181 0.000432148073507337 0.00016744175505828 ... 100 | 2.19939355821262e-05 1.74036768914518e-05 0.000163090674713188 ... 101 | 0.000429891458439764 0.000778683858845478 0.00114268243581356 ... 102 | 0.00146157429683154 0.00166758426354622 0.00172032070023352 ... 103 | 0.00158991243665027 0.00128564832641177 0.000831877941347418 ... 104 | 0.000287841116282626 -0.000284585429523636 -0.00080741790828161 ... 105 | -0.00122137372149596 -0.00147255967554081 -0.00154382233228169 ... 106 | -0.00143470718708419 -0.00118457779739524 -0.000843022243028721 ... 107 | -0.000485487921272496 -0.000177602067978723 1.25733084470258e-05 ... 108 | 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| -0.00845589366515759 -0.00675302881169255 -0.0043329565381314 ... 164 | -0.00147920271415135 0.00144948780227816 0.00407922920951518 ... 165 | 0.00607325386708158 0.00719238354259494 0.00732999025194033 ... 166 | 0.0065389352067878 0.00502235705966584 0.00311120909414423 ... 167 | 0.00120962214100008 -0.00026322894313479 -0.000946757158031066 ... 168 | -0.000595889550445974 0.000867452233142299 0.00333711802333145 ... 169 | 0.00652456436187785 0.00999906385409933 0.0132434492793068 ... 170 | 0.0157355933592811 0.0170259782914805 0.0168168116652252 ... 171 | 0.0150129163049516 0.011752153411042 0.00739260459914801 ... 172 | 0.00247276622542218 -0.00236650516682364 -0.00647349032034802 ... 173 | -0.00929107489729222 -0.0104473173019083 -0.00982987487568419 ... 174 | -0.00761855864556878 -0.00428129043041928 -0.000519406754297763 ... 175 | 0.00282115812767937 0.00487273792305238 0.00487691550117553 ... 176 | 0.00231486289559252 -0.00299170757852051 -0.0108155986787147 ... 177 | 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0.000310142715254736 0.000261396848418661 ... 288 | 0.000104535760632245 -0.00013640388116154 -0.000436731328019749 ... 289 | -0.000747926712404859 -0.00103218319469495 -0.00124088094652763 ... 290 | -0.0013508056561749 -0.00133949776678719 -0.00121536741674112 ... 291 | -0.000990638264621241 -0.000705418250258672 -0.000393624800223724 ... 292 | -0.000107297154159663 0.000122642674771688 0.000260150732111351 ... 293 | 0.000300152076643192 0.000238970209283056 0.00010397416415684 ... 294 | -7.97707623321105e-05 -0.000264866620382325 -0.000418096411620716 ... 295 | -0.000497679147040258 -0.000488291423316057 -0.000374723448002457 ... 296 | -0.000172586318883111 0.000101859272868335 0.000406087327598929 ... 297 | 0.000706959534604148 0.000957920423536317 0.00113315427677536 ... 298 | 0.00120409027056389 0.00117305271532155 0.00104366851800283 ... 299 | 0.00084639122956572 0.000609593950013892 0.000378473075186087 ... 300 | 0.000181057233804151 5.36711665057605e-05 2.84290364384147e-06 ... 301 | 3.73914030709788e-05 0.000134492374813304 0.000275080671860863 ... 302 | 0.000416214572303194 0.000528894501482778 0.000573536786512098 ... 303 | 0.000536706886217158 0.000404014834464624 0.000191937587439258 ... 304 | -8.33641642906061e-05 -0.000378573620663888 -0.000658580757554115 ... 305 | -0.000874321142778759 -0.00100006055139718 -0.00100875558385843 ... 306 | -0.000903766902394608 -0.000693177160645144 -0.000418648226511807 ... 307 | -0.000119583148643723 0.000147883148801144 0.000336501435816117 ... 308 | 0.000399943444625612 0.000321115610522015 8.73201469754458e-05 ... 309 | -0.000272813899063807 -0.000728040883331772 -0.00121232311086121 ... 310 | -0.00166745545718656 -0.002017236905325 -0.00221004575422702 ... 311 | -0.00219418161359554 -0.00195693426079309 -0.00149634900145798 ... 312 | -0.000853338810487761 -7.67217364537999e-05 0.000752988044242502 ... 313 | 0.0015636605122513 0.0022707550011296 0.0028145334147178 ... 314 | 0.00313705089397488 0.0032172861845596 0.00305067423893676 ... 315 | 0.0026744932687209 0.00212920622287149 0.00147102389173814 ... 316 | 0.000779724453430603 0.000117220153030299 -0.000463578741140334 ... 317 | -0.000910323983903008 -0.0012114325405598 -0.00135240424690182 ... 318 | -0.00135886579937377 -0.00124993663037054 -0.00107386143967476 ... 319 | -0.000862130982321538 -0.000662713612794409 -0.000496899333793406 ... 320 | -0.000394179580544488 -0.000353545485975799 -0.0003816461205007 ... 321 | -0.000458292846564782 -0.000577035216303767 -0.000712726807495988 ... 322 | -0.000862114418365961 -0.0010107443034646 -0.00117137124297013 ... 323 | -0.00134843843172702 -0.00157370713114779 -0.00186851816397141 ... 324 | 0.00441355600153117]); 325 | end 326 | 327 | y = step(Hd,double(x)); 328 | 329 | 330 | % [EOF] 331 | -------------------------------------------------------------------------------- /lowPassFilter12_5G.m: -------------------------------------------------------------------------------- 1 | function y = lowPassFilter12_5G(x) 2 | %LOWPASSFILTER12.5G Filters input x and returns output y. 3 | 4 | % MATLAB Code 5 | % Generated by MATLAB(R) 9.1 and the DSP System Toolbox 9.3. 6 | % Generated on: 31-Jul-2017 10:04:18 7 | 8 | %#codegen 9 | 10 | % To generate C/C++ code from this function use the codegen command. Type 11 | % 'help codegen' for more information. 12 | 13 | persistent Hd; 14 | 15 | if isempty(Hd) 16 | 17 | % The following code was used to design the filter coefficients: 18 | % % Equiripple Lowpass filter designed using the FIRPM function. 19 | % 20 | % % All frequency values are in GHz. 21 | % Fs = 600; % Sampling Frequency 22 | % 23 | % Fpass = 12; % Passband Frequency 24 | % Fstop = 13; % Stopband Frequency 25 | % Dpass = 0.057501127785; % Passband Ripple 26 | % Dstop = 0.0001; % Stopband Attenuation 27 | % dens = 20; % Density Factor 28 | % 29 | % % Calculate the order from the parameters using FIRPMORD. 30 | % [N, Fo, Ao, W] = firpmord([Fpass, Fstop]/(Fs/2), [1 0], [Dpass, Dstop]); 31 | % 32 | % % Calculate the coefficients using the FIRPM function. 33 | % b = firpm(N, Fo, Ao, W, {dens}); 34 | 35 | Hd = dsp.FIRFilter( ... 36 | 'Numerator', [-5.19572218698301e-05 1.34038611768226e-06 ... 37 | 2.81523738737816e-06 5.31579525727656e-06 8.9138629886297e-06 ... 38 | 1.36794135953464e-05 1.97103819240733e-05 2.70949465889774e-05 ... 39 | 3.59423930858887e-05 4.63456055692649e-05 5.84130757711627e-05 ... 40 | 7.22310124049068e-05 8.78961038325515e-05 0.00010547713480743 ... 41 | 0.000125048889994921 0.00014665293289413 0.000170332959510812 ... 42 | 0.000196094799429417 0.000223943570171715 0.000253842494409611 ... 43 | 0.000285752229166741 0.000319588106919302 0.000355261944619782 ... 44 | 0.000392637145774321 0.000431573598550562 0.00047188011961623 ... 45 | 0.000513363536939753 0.0005557779606463 0.000598879464856339 ... 46 | 0.000642371247953436 0.000685965762993885 0.000729323438356526 ... 47 | 0.000772124119742056 0.000813995323682612 0.000854596548401516 ... 48 | 0.000893529906765678 0.000930446435723571 0.000964926426599415 ... 49 | 0.000996633345487884 0.00102511504815639 0.00105010558370016 ... 50 | 0.0010713103122837 0.0010882457574968 0.0011007556077096 ... 51 | 0.00110850847676388 0.00111131215669776 0.00110894428055443 ... 52 | 0.00110127530382728 0.00108817819006551 0.00106960677079027 ... 53 | 0.00104553515672961 0.00101601306392714 0.000981121149606578 ... 54 | 0.000941012019828548 0.000895875220133563 0.000845967742367757 ... 55 | 0.000791585456629679 0.000733085600974945 0.000670862651564817 ... 56 | 0.00060536436059475 0.000537071190150744 0.000466507067109947 ... 57 | 0.000394221726162343 0.000320796875896394 0.000246830944688066 ... 58 | 0.000172940982338169 9.97496065514023e-05 2.78829600389123e-05 ... 59 | -4.20397979173812e-05 -0.00010941167993467 -0.000173644840885704 ... 60 | -0.000234178689804558 -0.000290482540320047 -0.00034206740594284 ... 61 | -0.000388484128758465 -0.000429341131249532 -0.000464295336408892 ... 62 | -0.0004930780587625 -0.000515469873381645 -0.000531344820569635 ... 63 | -0.00054058187280248 -0.000543212846949596 -0.000539308468480038 ... 64 | -0.000528986466104155 -0.00051247787378204 -0.000490050784840261 ... 65 | -0.000462062108592606 -0.000428913093033008 -0.000391078466061257 ... 66 | -0.000349072183004486 -0.000303466890248213 -0.000254864666159425 ... 67 | -0.000203910280125493 -0.000151264630438307 -9.76139838132342e-05 ... 68 | -4.36453913673104e-05 9.94697483149325e-06 6.24845455250485e-05 ... 69 | 0.000113299654535076 0.000161756402291545 0.00020724690531752 ... 70 | 0.000249210223719421 0.000287129002080868 0.000320546384880998 ... 71 | 0.000349062433595572 0.000372348689959331 0.000390143632256209 ... 72 | 0.000402264399509377 0.000408600907775647 0.000409124972665848 ... 73 | 0.000403883174271972 0.000393003642165245 0.000376687754584053 ... 74 | 0.000355214189597979 0.000328928299947571 0.000298242895508664 ... 75 | 0.000263625793611567 0.000225598904548853 0.000184726685051944 ... 76 | 0.000141616348121392 9.69141589697051e-05 5.12497745222144e-05 ... 77 | 5.30468686107646e-06 -4.02601624119975e-05 -8.47883961257419e-05 ... 78 | -0.00012763873374701 -0.000168199100328444 -0.000205890419525542 ... 79 | -0.000240179560968683 -0.000270582186941604 -0.000296674474615475 ... 80 | -0.000318094634199893 -0.000334552579277489 -0.000345829763631515 ... 81 | -0.000351786734802099 -0.000352360761933688 -0.000347570915295025 ... 82 | -0.000337513548222503 -0.000322365029215065 -0.00030237499703014 ... 83 | -0.000277866862919915 -0.000249228938322751 -0.000216912792999846 ... 84 | -0.000181422328158226 -0.000143310309553605 -0.000103165778631477 ... 85 | -6.16095422210969e-05 -1.9280628412759e-05 2.31687042171052e-05 ... 86 | 6.50872730573676e-05 0.000105830052082688 0.000144773240092925 ... 87 | 0.00018131882171071 0.000214909344235956 0.000245030023098756 ... 88 | 0.000271222816355885 0.000293085464015788 0.000310286901425007 ... 89 | 0.00032256324243541 0.000329738090130761 0.000331702138166661 ... 90 | 0.000328424763782203 0.00031997386309946 0.000306478649342363 ... 91 | 0.000288159305156317 0.000265304564674719 0.000238278436744378 ... 92 | 0.000207506848272248 0.000173477501760616 0.000136727254485323 ... 93 | 9.78380694054457e-05 5.74244467055659e-05 1.61270611139669e-05 ... 94 | -2.54001101961476e-05 -6.64975099539612e-05 -0.000106512467166543 ... 95 | -0.000144807439922193 -0.000180771827281919 -0.000213829994933527 ... 96 | -0.000243451896955303 -0.000269160340480835 -0.000290539890874662 ... 97 | -0.000307242627843561 -0.000318994695729764 -0.000325599967279512 ... 98 | -0.000326943930573414 -0.000322995275075763 -0.000313807112085906 ... 99 | -0.000299516332476349 -0.000280341909592513 -0.000256581758887732 ... 100 | -0.000228607653076341 -0.000196859923788365 -0.00016183992790681 ... 101 | -0.000124103805554762 -8.42525542394846e-05 -4.29249675447658e-05 ... 102 | -7.83598297098641e-07 4.14924704685025e-05 8.32252009041176e-05 ... 103 | 0.000123730181695015 0.000162352031632747 0.000198461048246347 ... 104 | 0.00023145998396935 0.000260806690180982 0.000286010244614461 ... 105 | 0.000306648725018918 0.000322368709380987 0.000332897962991971 ... 106 | 0.000338044173217138 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1.97103819240733e-05 1.36794135953464e-05 ... 541 | 8.9138629886297e-06 5.31579525727656e-06 2.81523738737816e-06 ... 542 | 1.34038611768226e-06 -5.19572218698301e-05]); 543 | end 544 | 545 | y = step(Hd,double(x)); 546 | 547 | 548 | % [EOF] 549 | --------------------------------------------------------------------------------