Add new functions
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Silas Oettinghaus
177
Classes/04_DSP/Sequence Detection/MLSE_viterbi.m
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177
Classes/04_DSP/Sequence Detection/MLSE_viterbi.m
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classdef MLSE_viterbi < handle
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%MLSE calculates the most probable sequence for an input signal with given/ known channel impulse response of any length
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properties(Access=public)
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M %PAM-M
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DIR
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trellis_states
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duobinary_output
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end
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methods (Access=public)
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function obj = MLSE_viterbi(options)
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%NAME Construct an instance of this class
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% Detailed explanation goes here
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arguments
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options.M double = 4;
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options.DIR double = [1];
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options.trellis_states double = [-3 -1 1 3];
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options.duobinary_output logical = false;
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end
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%
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fn = fieldnames(options);
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for n = 1:numel(fn)
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try
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obj.(fn{n}) = options.(fn{n});
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end
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end
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% do more stuff
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end
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function signalclass = process(obj,signalclass)
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data_in = signalclass.signal;
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data_out = obj.process_(data_in);
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signalclass.signal = data_out;
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end
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function data_out = process_(obj,data_in)
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% remove unnecessary zeros at start of impulse response to keep
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% number of trellis states minimal
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DIR_nonzero = find(obj.DIR ~= 0);
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if DIR_nonzero(1) > 1
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obj.DIR(1:DIR_nonzero(1)-1) = [];
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end
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if isscalar(obj.DIR)
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obj.DIR = [0 obj.DIR];
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%% WORKING
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% impulse respnse i.e. [0.5, 1.0000]
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obj.DIR = flip(obj.DIR);
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% RMS normalization of input data
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data_in = data_in ./ rms(data_in);
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% seems to be the only way to use combvec for a flexible amount
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% of vectors. 'combs' contains all trellis states
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pre_comb_mat = repmat(obj.trellis_states,length(obj.DIR)-1,1);
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pre_comb_cell = mat2cell(pre_comb_mat,ones(1,size(pre_comb_mat,1)),size(pre_comb_mat,2));
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combs = fliplr(combvec(pre_comb_cell{:}).');
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% Save first and last symbol of each state
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first_sym = combs(:,1);
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last_sym = combs(:,end);
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states = sum(combs,2);
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% Calculate all possible input symbols for the desired impulse
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% response. Row number is the index of the previous state,
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% column number is the index of the next state
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% noise free received == branch metrics
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noise_free_received = zeros(length(states),length(states));
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count_row = 1;
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count_col = 1;
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for l1 = 1:length(states)
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for l2 = 1:length(states)
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if sum(combs(l2,2:end) == combs(l1,1:end-1)) == size(combs,2)-1
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noise_free_received(count_row,count_col) = sum(combs(l2,:).*obj.DIR(end:-1:2)) + last_sym(l1)*obj.DIR(1);
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else
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noise_free_received(count_row,count_col) = inf;
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end
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count_row = count_row + 1;
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end
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count_col = count_col + 1;
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count_row = 1;
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end
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% match amplitude levels of input signal to those of the calculated ideal symbols
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% i.e. match the rms values of data_in to noise_free_received
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if isreal(data_in)
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if obj.M == round(obj.M)
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data_in = data_in * rms(noise_free_received(noise_free_received ~= inf),'all','omitnan');
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end
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end
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% OLD
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% initilaize the output vector
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data_out = NaN(size(data_in));
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sum_path_metrics = zeros(length(states),length(states));
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% first trellis path
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% euclidian distance as path metric
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path_metrics = (abs(repmat(data_in(1),size(noise_free_received)) - noise_free_received)).^2;
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sum_path_metrics = sum_path_metrics + path_metrics; % calculation of all possible sum path metrics
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[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
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% remaining trellis paths
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for n = 2:length(data_in)
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sum_path_metrics = repmat(sum_path_metrics_res(:,n-1).',length(states),1);
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% path_metrics = (repmat(data_in(n),size(noise_free_received)) - noise_free_received).^2;%.*prob_mat;
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path_metrics = (abs(repmat(data_in(n),size(noise_free_received)) - noise_free_received)).^2;
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sum_path_metrics = sum_path_metrics + path_metrics;
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[sum_path_metrics_res(:,n),path_idx(:,n)] = min(sum_path_metrics,[],2);
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end
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%% trace back
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ideal_path = NaN(1,length(data_in)+1);
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% find ideal trellis path by going through the trellis
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% backwards
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[~,ideal_path(length(data_in)+1)] = min(sum_path_metrics_res(:,length(data_in)));
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% starting with the state that has the lowest sum path
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% metric, follow the stored information about the
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% predecessor
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for h = length(data_in):-1:1
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ideal_path(h) = path_idx(ideal_path(h+1),h);
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end
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idx_out = ideal_path(2:length(data_in)+1);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%% WORKING
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if obj.duobinary_output
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%use duobinary encoder, output is already scaled inside this
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%one
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data_out = Duobinary().encode(first_sym(idx_out));
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else
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%
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data_out(1:length(data_in)) = first_sym(idx_out);
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% scale to rms = 1 using the standard sqrt() expressions
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if obj.M == 4
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data_out = data_out./sqrt(5);
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elseif obj.M == 6
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data_out = data_out./sqrt(10);
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elseif obj.M == 8
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data_out = data_out./sqrt(21);
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end
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end
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end
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end
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methods (Access=private)
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% Cant be seen from outside! So put all your functions here that can/
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% shall not be called from outside
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end
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end
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