Commit | Line | Data |
---|---|---|
5e9be3c4 AIL |
1 | function adaptFilterOut = adaptiveCMA(rSampled) |
2 | %% adaptive filter | |
3 | %% CMA | |
4 | taps = 19; % ODD taps | |
5 | hxx = zeros(taps, 1); | |
6 | %% hxx: real indices -K, ..., 0, ..., K. K = floor(taps/2) | |
7 | %% MATLAB indices 1 1+K taps | |
8 | %% initialize hxx, hxx[0] = 1, hxx[k] = hxx[-k] = 0 | |
9 | hxx(ceil(taps/2)) = 1; | |
10 | ||
11 | mu = 1e-3; | |
12 | numSymbs = length(rSampled); | |
13 | ||
14 | %% Check average energy of symbols | |
15 | rSampledUnitEnergy = normalizeEnergy(rSampled, numSymbs, 1); | |
16 | ||
17 | adaptFilterOut = zeros(numSymbs, 1); | |
18 | converged = 0; | |
19 | convergeCount = 0; | |
20 | ||
21 | for it = 1:numSymbs | |
22 | if it <= (taps - 1) / 2; | |
23 | xp = [zeros((taps - 1) / 2 - it + 1, 1); rSampledUnitEnergy(1:it + (taps - 1) / 2)]; | |
24 | elseif it + (taps - 1) / 2 > numSymbs | |
25 | xp = [rSampledUnitEnergy(it - (taps - 1) / 2 : end); zeros(it + (taps - 1) / 2 - numSymbs, 1)]; | |
26 | else | |
27 | xp = rSampledUnitEnergy(it - (taps - 1) / 2 : it + (taps - 1) / 2); | |
28 | end | |
29 | ||
30 | xout = sum(hxx .* xp); | |
31 | ex = 1 - abs(xout) ^ 2; | |
32 | ||
33 | if abs(ex) < 1e-3 | |
34 | convergeCount = convergeCount + 1; | |
35 | else | |
36 | convergeCount = 0; | |
37 | end | |
38 | if ~converged && convergeCount >= 10 | |
39 | converged = 1 | |
40 | it | |
41 | end | |
42 | ||
43 | adaptFilterOut(it) = xout; | |
44 | ||
45 | hxx = hxx + mu * ex * xout * conj(xp); | |
46 | end | |
47 | ||
48 | %{ | |
49 | %% try MATLAB builtin equalizer | |
50 | alg = cma(mu); | |
51 | eqObj = lineareq(taps, alg); | |
52 | eqObj.Weights((taps + 1) / 2) = 1; | |
53 | rPadded = [rSampledUnitEnergy; zeros((taps - 1) / 2, 1)]; | |
54 | matlabEq = equalize(eqObj, rPadded); | |
55 | matlabEq = matlabEq((taps + 1) / 2 : end); | |
56 | %} | |
57 | end |