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Silas
2024-09-06 15:53:32 +02:00
parent 03bfd70470
commit 179a7f9682
50 changed files with 921 additions and 93 deletions
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10
Classes/01_transmit/ChannelFreqResp.m
View File

@@ -47,9 +47,9 @@ classdef ChannelFreqResp < handle
% Detailed explanation goes here
arguments
options.Nacq = 4096;
options.Navg = 30;
options.Ncp = 100;
options.Nacq = 1024;
options.Navg = 64;
options.Ncp = 63;
options.f_ref;
options.f_observe;
end
@@ -188,8 +188,12 @@ classdef ChannelFreqResp < handle
if 0
figure(7);hold on;plot(fnew,20*log10(abs(iH)))
end
% iH(1) is DC ---> iH(end) is High Freq.
H_inv = [iH(1) iH fliplr(conj(iH)) conj(iH(1))];
H_inv = [iH(1) iH iH(end) fliplr(conj(iH)) conj(iH(1))];
obj.H_apply = H_inv;
Target.signal = real((ifft( ( fft(real( Target.signal )) .* H_inv' ) )));

35
Classes/01_transmit/PAMmapper.m
View File

@@ -6,6 +6,8 @@ classdef PAMmapper
M
unipolar
thresholds
levels
scaling
end
methods
@@ -17,6 +19,10 @@ classdef PAMmapper
obj.thresholds = obj.get_demodulation_thresholds();
obj.levels = obj.get_levels();
obj.scaling = rms(obj.get_levels());
end
@@ -111,6 +117,19 @@ classdef PAMmapper
%28.03.2023 - Silas Oett. - Extracted from digi_demod.m
%
% switch obj.M
% case 2
% thres = 0;
% case 4
% thres = [-2 0 2];
% case 6
% thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
% case 8
% thres = [-6 -4 -2 0 2 4 6];
% case 16
% thres = [-10 -8 -6 -4 -2 0 2 4 6 8 10];
% end
switch obj.M
@@ -159,6 +178,22 @@ classdef PAMmapper
end
function levels = get_levels(obj)
switch obj.M
case 2
levels = [-1 1];
case 4
levels = [-3 -1 1 3];
case 6
levels = [-5 -3 -1 1 3 5];
case 8
levels = [-7 -5 -3 -1 1 3 5 7];
case 16
levels = [-11 -9 -7 -5 -3 -1 1 3 5 7 9 11];
end
end
function [data_out] = demap_(obj,data_in)
data_in= data_in';
if obj.M ~= 6

83
Classes/04_DSP/Coding/Duobinary.m
View File

@@ -11,27 +11,17 @@ classdef Duobinary
function obj = Duobinary()
%NAME Construct an instance of this class
% Detailed explanation goes here
arguments
end
% %
% fn = fieldnames(options);
% for n = 1:numel(fn)
% try
% obj.(fn{n}) = options.(fn{n});
% end
% end
% do more stuff
end
function signalclass = precode(~,signalclass)
function signal = precode(~,signal)
if isa(signal,'Signal')
data = signal.signal;
else
data = signal;
end
data = signalclass.signal;
u = unique(data);
M = numel(u);
@@ -66,21 +56,34 @@ classdef Duobinary
bk = bk + b;
if M == 4
bk = bk ./ sqrt(5);
bk = bk ./ sqrt(5);
elseif M == 6
bk = bk ./ sqrt(10);
elseif M == 8
bk = bk ./ sqrt(21);
bk = bk ./ sqrt(21);
end
assert(isequal(unique(bk),u),'Check Duobinary Precoding'); %seems the signal is not the same as before
if isa(signal,'Signal')
signal.signal = bk;
else
signal = bk;
end
signalclass.signal = bk;
assert(isequal(unique(signalclass.signal),u),'Check Duobinary Precoding'); %seems the signal is not the same as before
end
function signalclass = encode(~,signalclass)
data = signalclass.signal;
function signal = encode(~,signal)
if isa(signal,'Signal')
data = signal.signal;
else
data = signal;
end
data = data./rms(data);
u = unique(data);
M = numel(u);
@@ -102,7 +105,7 @@ classdef Duobinary
data = data ./ 2;
assert(isequal((0:M-1)',unique(data)),'Check Duobinary Precoding'); %seems the signal is not unipolar
% duobinary coding (1+D)
coeff = [1,1];
@@ -119,7 +122,11 @@ classdef Duobinary
data = data ./ sqrt(10.5); % 15-level constellation weighted with probability after DB code i.e.
end
signalclass.signal = data;
if isa(signal,'Signal')
signal.signal = data;
else
signal = data;
end
% Code to evaluate the s in sqrt(s):
% M=6;
@@ -138,7 +145,7 @@ classdef Duobinary
data = signalclass.signal;
u = unique(data);
I = numel(u); %number of duobinary coded const. points
M = (I+1)/2; %PAM-M order
M = (I+1)/2; %PAM-M order
%make unipolar
if I == 7
@@ -166,11 +173,11 @@ classdef Duobinary
data = data - round(mean(data));
if M == 4
data = data ./ sqrt(5);
data = data ./ sqrt(5);
elseif M == 6
data = data ./ sqrt(10);
elseif M == 8
data = data ./ sqrt(21);
data = data ./ sqrt(21);
end
signalclass.signal = data;
@@ -180,7 +187,7 @@ classdef Duobinary
data = signalclass.signal;
u = unique(data);
I = numel(u); %number of duobinary coded const. points
M = (I+1)/2; %PAM-M order
M = (I+1)/2; %PAM-M order
%make unipolar
if I == 7
@@ -206,11 +213,11 @@ classdef Duobinary
%%FALSCH!
for k = 1:length(data)-1
d_ = data(k+1) - data_out(k);
[~,b] = min(abs(d_-[0:M-1]));
d_ = data(k+1) - data_out(k);
data_out(k+1) = const(b);
[~,b] = min(abs(d_-[0:M-1]));
data_out(k+1) = const(b);
end
@@ -219,13 +226,13 @@ classdef Duobinary
data_out = data_out - round(mean(data_out));
if M == 4
data_out = data_out ./ sqrt(5);
data_out = data_out ./ sqrt(5);
elseif M == 6
data_out = data_out ./ sqrt(10);
elseif M == 8
data_out = data_out ./ sqrt(21);
data_out = data_out ./ sqrt(21);
end
signalclass.signal = data_out;

165
Classes/04_DSP/Sequence Detection/MLSE.m Normal file
View File

@@ -0,0 +1,165 @@
classdef MLSE
%MLSE calculates the most probable sequence for an input signal with given/ known channel impulse response of any length
properties(Access=public)
M %PAM-M
DIR
trellis_states
duobinary_output
end
methods (Access=public)
function obj = MLSE(options)
%NAME Construct an instance of this class
% Detailed explanation goes here
arguments
options.M double = 4;
options.DIR double = [1];
options.trellis_states double = [-3 -1 1 3];
options.duobinary_output logical = false;
end
%
fn = fieldnames(options);
for n = 1:numel(fn)
try
obj.(fn{n}) = options.(fn{n});
end
end
% do more stuff
end
function signalclass = process(obj,signalclass)
data_in = signalclass.signal;
data_out = obj.process_(data_in);
signalclass.signal = data_out;
end
function data_out = process_(obj,data_in)
% remove unnecessary zeros at start of impulse response to keep
% number of trellis states minimal
DIR_nonzero = find(obj.DIR ~= 0);
if DIR_nonzero(1) > 1
obj.DIR(1:DIR_nonzero(1)-1) = [];
end
if length(obj.DIR) == 1
obj.DIR = [0 obj.DIR];
end
% RMS normalization of input data
data_in = data_in ./ rms(data_in);
% seems to be the only way to use combvec for a flexible amount
% of vectors. 'combs' contains all trellis states
pre_comb_mat = repmat(obj.trellis_states,length(obj.DIR)-1,1);
pre_comb_cell = mat2cell(pre_comb_mat,ones(1,size(pre_comb_mat,1)),size(pre_comb_mat,2));
combs = fliplr(combvec(pre_comb_cell{:}).');
% Save first and last symbol of each state
first_sym = combs(:,1);
last_sym = combs(:,end);
states = sum(combs,2);
% Calculate all possible input symbols for the desired impulse
% response. Row number is the index of the previous state,
% column number is the index of the next state
noise_free_received = zeros(length(states),length(states));
count_row = 1;
count_col = 1;
for l1 = 1:length(states)
for l2 = 1:length(states)
if sum(combs(l2,2:end) == combs(l1,1:end-1)) == size(combs,2)-1
noise_free_received(count_row,count_col) = sum(combs(l2,:).*obj.DIR(end:-1:2)) + last_sym(l1)*obj.DIR(1);
else
noise_free_received(count_row,count_col) = inf;
end
count_row = count_row + 1;
end
count_col = count_col + 1;
count_row = 1;
end
% match amplitude levels of input signal to those of the calculated ideal symbols
% i.e. match the rms values of data_in to noise_free_received
if isreal(data_in)
if obj.M == round(obj.M)
data_in = data_in * rms(noise_free_received,'all','omitnan');
end
end
% initilaize the output vector
data_out = NaN(size(data_in));
sum_path_metrics = zeros(length(states),length(states));
% first trellis path
% euclidian distance as path metric
path_metrics = (abs(repmat(data_in(1),size(noise_free_received)) - noise_free_received)).^2;
sum_path_metrics = sum_path_metrics + path_metrics; % calculation of all possible sum path metrics
[sum_path_metrics_res(:,1),path_idx(:,1)] = min(sum_path_metrics,[],2); % find the best path to each state, store sum path metric and predecessor for each state
% remaining trellis paths
for n = 2:length(data_in)
sum_path_metrics = repmat(sum_path_metrics_res(:,n-1).',length(states),1);
% path_metrics = (repmat(data_in(n),size(noise_free_received)) - noise_free_received).^2;%.*prob_mat;
path_metrics = (abs(repmat(data_in(n),size(noise_free_received)) - noise_free_received)).^2;
sum_path_metrics = sum_path_metrics + path_metrics;
[sum_path_metrics_res(:,n),path_idx(:,n)] = min(sum_path_metrics,[],2);
end
%% trace back
ideal_path = NaN(1,length(data_in)+1);
% find ideal trellis path by going through the trellis
% backwards
[~,ideal_path(length(data_in)+1)] = min(sum_path_metrics_res(:,length(data_in)));
% starting with the state that has the lowest sum path
% metric, follow the stored information about the
% predecessor
for h = length(data_in):-1:1
ideal_path(h) = path_idx(ideal_path(h+1),h);
end
idx_out = ideal_path(1:length(data_in));
if obj.duobinary_output
%use duobinary encoder, output is already scaled inside this
%one
data_out = Duobinary().encode(first_sym(idx_out));
else
%
data_out(1:length(data_in)) = first_sym(idx_out);
% scale to rms = 1 using the standard sqrt() expressions
if obj.M == 4
data_out = data_out./sqrt(5);
elseif obj.M == 6
data_out = data_out./sqrt(10);
elseif obj.M == 8
data_out = data_out./sqrt(21);
end
end
end
end
methods (Access=private)
% Cant be seen from outside! So put all your functions here that can/
% shall not be called from outside
end
end