89 lines
3.6 KiB
Matlab
89 lines
3.6 KiB
Matlab
% This script is used to evaluate Fig. 1b) in the paper "Adaptive Removal of Multipath Interference in Short Reach 112 GBd PAM-4 IM/DD Systems"
|
||
|
||
%% Parameters
|
||
df = 1e6; % Laser linewidth [Hz]
|
||
SIR_dB = 20; % Interference attenuation [dB]
|
||
alpha = 10^(-SIR_dB/20); % Interference attenuation [linear]
|
||
n_fiber = 1.467; % Refractive index
|
||
c = physconst('lightspeed'); % [m/s]
|
||
|
||
L = linspace(0,250,50); % Interference delay [m]
|
||
tau = n_fiber./c.*L; % Interference time (= tau) [s]
|
||
|
||
tau_c = 1/(pi*df); % laser coherence time [s]
|
||
L_c = (c/n_fiber)*tau_c; % laser coherence length [m]
|
||
|
||
var_sat = 2*alpha^2; % Analytical saturation of variance
|
||
|
||
%% Monte–Carlo Simulation
|
||
fs = 100e9; % sampling rate [Hz]
|
||
Tsim = 50e-6; % sim duration [s]
|
||
N = round(Tsim*fs); % number of samples for each realization
|
||
max_delay_samples = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay)
|
||
phase_noise_std = sqrt(2*pi*df/fs); % standard dev. phase noise
|
||
|
||
num_realizations = 50; % number of parallel runs
|
||
monte_carlo_variance = zeros(num_realizations, length(L));
|
||
parfor r = 1:num_realizations
|
||
|
||
% generate a realization of phase noise random walk
|
||
dphi = phase_noise_std * randn(1, N + max_delay_samples); % matlab randn process has std = 1
|
||
phi = cumsum(dphi);
|
||
phi_direct = phi(max_delay_samples+1 : max_delay_samples+N);
|
||
var_k = zeros(1, length(L));
|
||
for t = 1:length(tau)
|
||
|
||
nd = round( tau(t)*fs ); % delay in samples for current interference time
|
||
phi_delayed = phi(max_delay_samples+1-nd : max_delay_samples+N-nd); %cut out interfering signal part (was earlier)
|
||
|
||
E = exp(1j*phi_direct) + alpha*exp(1j*phi_delayed); % E-fields combined
|
||
I = abs(E).^2; % photo current as magnitude square of E-field
|
||
var_k(t) = var(I);
|
||
|
||
end
|
||
monte_carlo_variance(r, :) = var_k;
|
||
end
|
||
|
||
avg_of_mc_variances = mean(monte_carlo_variance, 1);
|
||
std_of_mc_variances = std(monte_carlo_variance, 0, 1);
|
||
|
||
%% Analytic variance
|
||
L_ = linspace(0,250,500); % Interference delay [m]
|
||
tau_ = n_fiber./c.*L_;
|
||
analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau_)).^2;
|
||
|
||
%% Plot
|
||
cols = [0.3467 0.5360 0.6907
|
||
0.9153 0.2816 0.2878
|
||
0.4416 0.7490 0.4322];
|
||
|
||
coherence_length_multiples = 0.5:0.5:ceil(L(end)/L_c);
|
||
|
||
figure();
|
||
hold on;
|
||
[hl, hp] = boundedline(L, avg_of_mc_variances, std_of_mc_variances, 'alpha', 'cmap', cols(1,:));
|
||
set(hl, 'LineWidth', 2, 'DisplayName', 'Simulation');
|
||
set(hp, 'HandleVisibility', 'off', 'FaceAlpha', 0.8); % Hide patch from legend to match original behavior
|
||
|
||
|
||
plot(L_, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
|
||
xticks(coherence_length_multiples.*L_c);
|
||
xticklabels(round(coherence_length_multiples.*L_c,1));
|
||
|
||
norm_to_coherence_len = 1;
|
||
if norm_to_coherence_len
|
||
xticklabels(coherence_length_multiples);
|
||
xlabel('$n \cdot L_c$', 'FontSize',12);
|
||
else
|
||
xlabel('Interference Delay [m]', 'FontSize',12);
|
||
end
|
||
|
||
%xline(L_c.*coherence_length_multiples, 'LineWidth',1.5,'HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-');
|
||
xlim([0,L(end)]);
|
||
yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$');
|
||
grid on;
|
||
ylabel('Intensity Variance', 'FontSize',12);
|
||
title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14);
|
||
legend('Location','southeast');
|
||
|
||
% mat2tikz_improved("C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mpi\analytical_mpi_variance2.tikz"); |