| 1 | numSymbs = 5e5; |
| 2 | M = 4; |
| 3 | |
| 4 | Rsym = 2.5e10; % symbol rate (sym/sec) |
| 5 | Tsym = 1 / Rsym; % symbol period (sec) |
| 6 | |
| 7 | rolloff = 0.25; |
| 8 | span = 6; % filter span |
| 9 | sps = 2; % samples per symbol |
| 10 | |
| 11 | fs = Rsym * sps; % sampling freq (Hz) |
| 12 | Tsamp = 1 / fs; |
| 13 | |
| 14 | t = (0 : 1 / fs : numSymbs / Rsym + (1.5 * span * sps - 1) / fs).'; |
| 15 | |
| 16 | |
| 17 | %%power_dBm = -3:0.2:4; |
| 18 | power_dBm = [0]; |
| 19 | power = 10 .^ (power_dBm / 10) * 1e-3; % watts |
| 20 | |
| 21 | Es = power * Tsym; % joules |
| 22 | Eb = Es / log2(M); % joules |
| 23 | |
| 24 | N0ref_db = 10; % Eb/N0 at power = 1mW |
| 25 | %% Fix N0, such that Eb/N0 = N0ref_db at power = 1mW |
| 26 | N0 = 1e-3 * Tsym / (log2(M) * 10 ^ (N0ref_db / 10)); % joules |
| 27 | |
| 28 | |
| 29 | plotlen = length(power); |
| 30 | |
| 31 | ber = zeros(1, plotlen); |
| 32 | |
| 33 | data = randi([0 M - 1], numSymbs, 1); |
| 34 | modData = pskmod(data, M, pi / M, 'gray'); |
| 35 | |
| 36 | |
| 37 | %% Chromatic dispersion |
| 38 | D = 17; % ps / (nm km) |
| 39 | lambda = 1550; % nm |
| 40 | z = 600; % km |
| 41 | |
| 42 | |
| 43 | for i = 1:plotlen |
| 44 | snr = Es(i) / sps / N0; |
| 45 | snr_dB = 10 * log10(snr); |
| 46 | |
| 47 | x = txFilter(modData, rolloff, span, sps); |
| 48 | %% Now, sum(abs(x) .^ 2) / length(x) should be 1. |
| 49 | %% We can set its power simply by multiplying. |
| 50 | x = sqrt(power(i)) * x; |
| 51 | |
| 52 | %% We can now do split-step Fourier. |
| 53 | gamma = 1.2; % watt^-1 / km |
| 54 | %%stepnum = round(40 * z * gamma); % Nonlinear Fiber optics, App B |
| 55 | stepnum = 100; |
| 56 | xCD = splitstepfourier(x, D, lambda, z, Tsamp, gamma, stepnum); |
| 57 | |
| 58 | y = awgn(xCD, snr, power(i), 'linear'); |
| 59 | %%y = xCD; |
| 60 | |
| 61 | r = rxFilter(y, rolloff, span, sps); |
| 62 | rCDComp = CDCompensation(r, D, lambda, z, Tsamp); |
| 63 | rCDComp = normalizeEnergy(rCDComp, numSymbs*sps, 1); |
| 64 | |
| 65 | rSampled = rCDComp(sps*span/2+1:sps:(numSymbs+span/2)*sps); |
| 66 | rNoCompSampled = r(sps*span/2+1:sps:(numSymbs+span/2)*sps); |
| 67 | |
| 68 | %% rotate rNoCompSampled to match original data |
| 69 | theta = angle(-sum(rNoCompSampled .^ M)) / M; |
| 70 | %% if theta approx +pi/M, wrap to -pi/M |
| 71 | if abs(theta - pi / M) / (pi / M) < 0.1 |
| 72 | theta = -pi / M; |
| 73 | end |
| 74 | rNoCompSampled = rNoCompSampled .* exp(-j * theta); |
| 75 | |
| 76 | |
| 77 | %% Not entirely sure why, but after using FFT instead of time-domain |
| 78 | %% convolution for simulating CD, we now need to do the same rotation |
| 79 | %% for rSampled as well, but this time with a positive rotation. |
| 80 | theta = angle(-sum(rSampled .^ M)) / M; |
| 81 | if abs(theta + pi / M) / (pi / M) < 0.1 |
| 82 | theta = +pi / M; |
| 83 | end |
| 84 | rSampled = rSampled .* exp(-1j * theta); |
| 85 | |
| 86 | %% adaptive filter |
| 87 | adaptFilterOut = adaptiveCMA(rSampled); |
| 88 | |
| 89 | demodAdapt = pskdemod(adaptFilterOut, M, pi / M, 'gray'); |
| 90 | [~, ber(i)] = biterr(data, demodAdapt) |
| 91 | end |
| 92 | |
| 93 | return |
| 94 | |
| 95 | |
| 96 | figure(1); |
| 97 | clf; |
| 98 | |
| 99 | %% Plot simulated results |
| 100 | semilogy(power_dBm, ber, 'Color', [0, 0.6, 0], 'LineWidth', 2); |
| 101 | hold on; |
| 102 | |
| 103 | title({'CD + Kerr + CD compensation', ... |
| 104 | strcat(['$D = 17$ ps/(nm km), $z = ', num2str(z), '$ km'])}); |
| 105 | grid on; |
| 106 | %%xlabel('$E_b/N_0$ (dB)'); |
| 107 | xlabel('Optical power (dBm)'); |
| 108 | ylabel('BER'); |
| 109 | |
| 110 | formatFigure; |