From 0a69fdc071a1e17e37526c1c26b338477b1c20bd Mon Sep 17 00:00:00 2001 From: DoHaiSon Date: Tue, 1 Aug 2023 17:11:30 +0700 Subject: [PATCH] #29: first version of B-EM and SB-EM --- .../Algo_Mode/Blind/EM/B_EM_Nonlinear_MIMO.m | 176 +++++++++++++++++ .../EM/Params/B_EM_Nonlinear_MIMO_params.m | 55 ++++++ .../Algo_Mode/Blind/EM/Params/B_EM_func.m | 59 ++++++ .../Algo_Mode/Blind/SS/Params/B_SS_params.m | 2 +- .../Non-blind/ZF/Params/NB_ZF_params.m | 2 +- .../Semi-blind/EM/Params/SB_EM_Dec2Alphabet.m | 15 ++ .../Semi-blind/EM/Params/SB_EM_EstimeCov.m | 32 +++ .../EM/Params/SB_EM_FindTransitionsPred.m | 30 +++ .../EM/Params/SB_EM_FindTransitionsPredMIMO.m | 66 +++++++ .../EM/Params/SB_EM_FindTransitionsSuccess.m | 30 +++ .../Params/SB_EM_FindTransitionsSuccessMIMO.m | 66 +++++++ .../EM/Params/SB_EM_Nonlinear_MIMO_params.m | 55 ++++++ .../EM/Params/SB_EM_PredecessorsMIMO.m | 42 ++++ .../EM/Params/SB_EM_PredecessorsSIMO.m | 28 +++ .../EM/Params/SB_EM_SuccessorsMIMO.m | 43 +++++ .../EM/Params/SB_EM_SuccessorsSIMO.m | 29 +++ .../Semi-blind/EM/Params/SB_EM_alpha_beta2.m | 44 +++++ .../Semi-blind/EM/Params/SB_EM_cal_K.m | 35 ++++ .../Semi-blind/EM/Params/SB_EM_cal_frac.m | 18 ++ .../Semi-blind/EM/Params/SB_EM_cal_param.m | 42 ++++ .../EM/Params/SB_EM_cal_parametres.m | 30 +++ .../Semi-blind/EM/Params/SB_EM_cal_prob2.m | 21 ++ .../Semi-blind/EM/Params/SB_EM_cal_sigmaH_B.m | 59 ++++++ .../EM/Params/SB_EM_cal_sigmaH_SB.m | 61 ++++++ .../Semi-blind/EM/Params/SB_EM_cal_tab.m | 29 +++ .../Semi-blind/EM/Params/SB_EM_cal_tableaux.m | 67 +++++++ .../Semi-blind/EM/Params/SB_EM_cal_theta.m | 79 ++++++++ .../Semi-blind/EM/Params/SB_EM_cal_trans.m | 26 +++ .../Semi-blind/EM/Params/SB_EM_cal_trans2.m | 21 ++ .../Semi-blind/EM/Params/SB_EM_calcH.m | 35 ++++ .../Semi-blind/EM/Params/SB_EM_compute_nmse.m | 13 ++ .../Semi-blind/EM/Params/SB_EM_estim_H.m | 25 +++ .../Semi-blind/EM/Params/SB_EM_func.m | 57 ++++++ .../EM/Params/SB_EM_min_symbol_errorMIMO.m | 38 ++++ .../Semi-blind/EM/Params/SB_EM_predecesseur.m | 23 +++ .../Semi-blind/EM/Params/SB_EM_successeur.m | 22 +++ .../Semi-blind/EM/Params/SB_EM_transitions.m | 12 ++ .../Semi-blind/EM/SB_EM_Nonlinear_MIMO.m | 182 ++++++++++++++++++ .../Semi-blind/MRE/Params/SB_MRE_rc_params.m | 4 +- Resource/Dashboard/Default.png | Bin 210499 -> 210499 bytes Shared/General/ER_func.m | 4 +- 41 files changed, 1671 insertions(+), 6 deletions(-) create mode 100644 Algorithms/Algo_Mode/Blind/EM/B_EM_Nonlinear_MIMO.m create mode 100644 Algorithms/Algo_Mode/Blind/EM/Params/B_EM_Nonlinear_MIMO_params.m create mode 100644 Algorithms/Algo_Mode/Blind/EM/Params/B_EM_func.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Dec2Alphabet.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_EstimeCov.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPred.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPredMIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccess.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccessMIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Nonlinear_MIMO_params.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsMIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsSIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsMIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsSIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_alpha_beta2.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_K.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_frac.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_param.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_parametres.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_prob2.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_B.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_SB.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tab.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tableaux.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_theta.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_trans.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_trans2.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_calcH.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_compute_nmse.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_estim_H.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_func.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_min_symbol_errorMIMO.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_predecesseur.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_successeur.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_transitions.m create mode 100644 Algorithms/Algo_Mode/Semi-blind/EM/SB_EM_Nonlinear_MIMO.m diff --git a/Algorithms/Algo_Mode/Blind/EM/B_EM_Nonlinear_MIMO.m b/Algorithms/Algo_Mode/Blind/EM/B_EM_Nonlinear_MIMO.m new file mode 100644 index 0000000..1b9e26b --- /dev/null +++ b/Algorithms/Algo_Mode/Blind/EM/B_EM_Nonlinear_MIMO.m @@ -0,0 +1,176 @@ +function Err = B_EM_Nonlinear_MIMO(Op, SNR_i, Output_type) + +%% Blind Expectation-Maximization for Non-linear MIMO communications +% +%% Input: + % + 1. Ns: number of sample data + % + 2. Nt: number of transmit antennas + % + 3. Nr: number of receive antennas + % + 4. M: length of the channel + % + 5. Ch_type: type of the channel (real, complex, + % user' input + % + 6. Mod_type: type of modulation (All) + % + 7. Threshold: threshold of EM algorithm + % + 8. Np: number of pilot symbols + % + 9. N_iter_max: number of maximum iterations EM + % + 10. SNR_i: signal noise ratio + % + 11. Output_type: SER / BER / MSE Channel +% +%% Output: + % + 1. Err: SER / BER / MSE Channel +% +%% Algorithm: + % Step 1: Initialize variables + % Step 2: Return +% +% Ref: Ouahbi Rekik, Karim Abed-Meraim, Mohamed Nait-Meziane, +% Anissa Mokraoui, and Nguyen Linh Trung, "Maximum likelihood +% based identification for nonlinear multichannel communications +% systems," Signal Processing, vol. 189, p. 108297, Dec. 2021. +% +%% Require R2006A + +% Author: Ouahbi Rekik, L2TI, UR 3043, Université Sorbonne Paris Nord, France + +% Adapted for InSI by Do Hai Son, 1-Aug-2023 +% InSI: A MatLab Toolbox for Informed System Identification in +% Wireless Communications +% https://avitech-vnu.github.io/InSI +% Project: NAFOSTED 01/2019/TN on Informed System Identification +% PI: Nguyen Linh Trung, Vietnam National University, Hanoi, Vietnam +% Co-PI: Karim Abed-Meraim, Université d’Orléans, France + + +% Initialize variables +Ns = Op{1}; % number of sig sequences +Nt = Op{2}; % number of transmit antennas +Nr = Op{3}; % number of receive antennas +M = Op{4}; % length of the channel +Ch_type = Op{5}; % complex +Mod_type = Op{6}; +threshold = Op{7}; % threshold of EM algorithm +Np = Op{8}; % number of pilot symbols +N_iter_max= Op{9}; % number of maximum iterations EM +Np_init = Np; + + +% Generate input signal +modulation = {'Bin', 'QPSK', 'QAM4', 'QAM16', 'QAM64', 'QAM128', 'QAM256'}; + +% Generate channel +if Ch_type == 2 + channels_mat = Generate_channel(Nr, Nt * 2*(M+1) - 1, Ch_type) * sqrt(2); +else + channels_mat = Generate_channel(Nr, Nt * 2*(M+1) - 1, Ch_type); +end +channels_vec = reshape(channels_mat, [], 1); % vectorize channel matrix + +% Generate signals +[sig_src, data] = eval(strcat(modulation{Mod_type}, '(1000)')); +alphabet = unique(sig_src); +P = length(alphabet); +average_energy_per_symb = norm(alphabet)^2/P; % or: 2/3*(P-1); +alphabet = alphabet/sqrt(average_energy_per_symb); % normalization + +%% transitions, predecessors, successors +trans1 = SB_EM_cal_trans(alphabet, Nt*(M+1)); +trans2 = trans1.^2; % quadratic model +trans = [trans1; trans2]; +nbr_etats = P^(Nt*M); %number of states +successors = zeros(nbr_etats,P^Nt); +predecessors = zeros(nbr_etats,P^Nt); +tableau = zeros(P^(Nt*(M+1)),2); % tableau(:,1)-->predecesseurs (i) ,tableau(:,2)-->successeurs (j) + +for i=1:nbr_etats + predecessors(i,:) = SB_EM_PredecessorsMIMO( i ,P, M, Nt ); + [itransPred,~] = SB_EM_FindTransitionsPredMIMO(predecessors(i,:),i,M, P, Nt ); + tableau(itransPred,2) = i; + + successors(i,:) = SB_EM_SuccessorsMIMO( i ,P, M, Nt ); + [itransSuccess,~] = SB_EM_FindTransitionsSuccessMIMO(i,successors(i,:),M, P, Nt ); + tableau(itransSuccess,1) = i; +end + +%% Transmitted signal +S = []; +D = []; + +for i = 1:Nt + [sig_src, data] = eval(strcat(modulation{Mod_type}, '(Ns + M)')); + S = [S; sig_src.']; + D = [D; data.']; +end +S_decim = D; +S = S/sqrt(average_energy_per_symb); + +Stop1 = toeplitz(S(1,M+1:-1:1),S(1,M+1:Ns+M)); +D_Stop1 = toeplitz(S_decim(1,M+1:-1:1),S_decim(1,M+1:Ns+M)); + +if Nt==1 + S_mat = [Stop1; Stop1.^2]; + + D_mat = [D_Stop1; D_Stop1.^2]; +else + Stop2 = toeplitz(S(2,M+1:-1:1),S(2,M+1:Ns+M)); + S_mat = [Stop1; Stop2; Stop1.^2; Stop2.^2]; + + D_Stop2 = toeplitz(S_decim(2,M+1:-1:1), S_decim(2,M+1:Ns+M)); + D_mat = [D_Stop1; D_Stop2; D_Stop1.^2; D_Stop2.^2]; +end + +%% Received signal +sig_cap_sans_bruit = channels_mat*S_mat; + +% ... pilots +Sp = []; +Dp = []; + +for i = 1:Nt + [sig_src, data] = eval(strcat(modulation{Mod_type}, '(Ns + M)')); + Sp = [Sp; sig_src.']; + Dp = [Dp; data.']; +end +Sp_decim = Dp; +Sp = Sp/sqrt(average_energy_per_symb); + +Stop1p = toeplitz(Sp(1,M+1:-1:1),Sp(1,M+1:Np+M)); +if Nt==1 + Sp_mat = [Stop1p; Stop1p.^2]; +else + Stop2p = toeplitz(Sp(2,M+1:-1:1), Sp(2,M+1:Np+M)); + Sp_mat = [Stop1p; Stop2p; Stop1p.^2; Stop2p.^2]; +end +sigp_cap_sans_bruit = channels_mat*Sp_mat; + +% --- received signal strength ---------------------------------- +Pr = norm(sig_cap_sans_bruit,'fro').^2/numel(sig_cap_sans_bruit); + +sigmav2 = 10^((10*log10(Pr)-SNR_i)/10); % noise variance for received signal + +noise = sqrt(sigmav2)*(randn(Nr,Ns) + randn(Nr,Ns)*1i)/sqrt(2); + +sig_cap = sig_cap_sans_bruit + noise; +sigcap_pilot = sigp_cap_sans_bruit + noise(:,1:Np); %noisep; + +%----------------- pilot-based initialization ------------------- +canauxp = sig_cap(:,1:Np_init)*pinv(S_mat(:,1:Np_init)); + +%-------------------------- B EM ------------------------------- +[probaB, ~, channels_mat_EM_B, ~] = ... + B_EM_func(sig_cap.', trans, canauxp, sigmav2, predecessors, ... + successors, tableau, N_iter_max, threshold, Nt); + +%% Vectorize estimated channels +channels_vec_EM_B = reshape(channels_mat_EM_B, [],1); % h_{EM-SB} semi-blind + +%% Data detection +est_src = SB_EM_min_symbol_errorMIMO(probaB, alphabet, M, Nt); + +% Compute SER / BER / MSE channel +if Output_type ~= 4 + Err = ER_func(D_mat(1,:).', est_src, Mod_type, Output_type); +else + Err = ER_func(channels_vec.', channels_vec_EM_B, Mod_type, Output_type); +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_Nonlinear_MIMO_params.m b/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_Nonlinear_MIMO_params.m new file mode 100644 index 0000000..eb39026 --- /dev/null +++ b/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_Nonlinear_MIMO_params.m @@ -0,0 +1,55 @@ +classdef B_EM_Nonlinear_MIMO_params + %Params Summary of this class goes here + % Detailed explanation goes here + + properties + % Parameters + num_params = 9 + params = {'No. samples', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation', 'Threshold', 'No. pilots', 'No. iter max'} + notations = {'Ns', 'Nt', 'Nr', 'M', 'ChType', 'Mod', 'threshold', 'Np', 'N_iter_max'} + tooltips = {} + % Type of the UIControl: edit_text = 1 + % popup_menu = 2 + % button = 3 + params_type = [1, 1, 1, 1, 2, 2, 1, 1, 1] + values = {100, 2, 4, 2, {'Real', 'Complex', 'Input'}, {'Binary', 'QPSK', '4-QAM', '16-QAM', '64-QAM', '128-QAM', '256-QAM'}, 0.001, 10, 20} + default_values = {100, 2, 4, 2, 2, 3, 0.001, 10, 20} + + % Default SNR and Monte + default_Monte = 10 + default_SNR = '-10:5:20' + + % Output + % Type of the outputs: SER Sig = 1 + % BER Sig = 2 + % MSE Sig = 3 + % MSE Cha = 4 + outputs = [1, 2, 4] + default_output = 1 + + % Figure + sys_model = 'Default.png' + title = {'B-EM Non-linear MIMO'} + xlabel = {'SNR (dB)', 'SNR (dB)', 'SNR (dB)', 'SNR (dB)'} + ylabel = {'SER', 'BER', 'MSE Signal (dB)', 'MSE Channel (dB)'} + trigger = false + position + linewidth = 1 + color = 'k' + + % Triggers/Flags + has_inter = [true, true, true, false, false, false, false, false, false] + rect = {} + rect_position = {[150 480 100 100], [260 1130 290 100], [1470 1130 290 100], 0, 0, 0, 0, 0, 0} + rect_linewidth = {2, 2, 2, 2, 2, 2, 2, 2, 2} + rect_color = {'b', 'g', 'r', 'b', 'r', 'g', 'g', 'b', 'b'} + + % Reference website + web_url = 'https://www.sciencedirect.com/science/article/abs/pii/S0165168421003340' + end + + methods (Access = private) + + end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_func.m b/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_func.m new file mode 100644 index 0000000..35582c3 --- /dev/null +++ b/Algorithms/Algo_Mode/Blind/EM/Params/B_EM_func.m @@ -0,0 +1,59 @@ +% Calcul des parametres (H,sigma) par l'algorithme EM maximisant la +% vraisemblance des signaux observes. +% +% SYNTAXE [proba,estsigma1,estcan1]=EM(sig_cap,trans,estcan,estsigma,predess,success,nb_iter,seuil) +% +% sig_cap : tableau des signaux recus par les capteurs +% trans : tableau ((M+1) X P^(M+1)) des transitions +% estcan : estimee initiale des coefficients des canaux +% estsigma : estimmee initiale de la puissance du bruit +% predess : tableau des predecesseurs d'etats +% success : tableau des successeurs d'etats +% nb_iter : nbr maximal d'iterations autorise +% seuil : seuil d'arret des iterations. +% +% sortie: +% proba : tableau des densites de probabilites L(Y,Xt=En) a la fin des iterations. +% estsigma1 : reestimee de sigma a la fin des iterations +% estcan1 : reestimee de can a la fin des iterations +% +function [proba,estsigma1,estcan1,compt] = B_EM_func(sig_cap,trans,estcan,estsigma,predess,success,tableau,nb_iter,seuil,Nt) + +old_vrais = -1; +test = seuil+1; +compt = 0; +M = length(estcan(1,:))/(2*Nt)-1; +Ncap = size(estcan,1); +P = (length(predess(1,:)))^(1/Nt); +T = length(sig_cap(:,1)); + +while(test > seuil) && (comptnew_vrais) + error('The likelihood is diminishing!!!') + end + + old_vrais = new_vrais; + [estsigma1,estcan1] = SB_EM_cal_sigmaH_B(proba,sig_cap,trans,Nt); + + vec_new = reshape(estcan1.',Ncap*Nt*2*(M+1),1); + vec_old = reshape(estcan.',Ncap*Nt*2*(M+1),1); + + test = norm(vec_new-vec_old)/norm(vec_old); %+abs(estsigma1-estsigma); + + estcan = estcan1; + estsigma1 = estsigma; + +end +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Blind/SS/Params/B_SS_params.m b/Algorithms/Algo_Mode/Blind/SS/Params/B_SS_params.m index 168a767..c6b96ef 100644 --- a/Algorithms/Algo_Mode/Blind/SS/Params/B_SS_params.m +++ b/Algorithms/Algo_Mode/Blind/SS/Params/B_SS_params.m @@ -5,7 +5,7 @@ properties % Parameters num_params = 6 - params = {'No. bits', 'No. channels', 'Channel order', 'Channel type', 'Modulation', 'Window length'} + params = {'No. samples', 'No. channels', 'Channel order', 'Channel type', 'Modulation', 'Window length'} notations = {'N', 'Nr', 'ChL', 'ChType', 'Mod', 'L'} tooltips = {} % Type of the UIControl: edit_text = 1 diff --git a/Algorithms/Algo_Mode/Non-blind/ZF/Params/NB_ZF_params.m b/Algorithms/Algo_Mode/Non-blind/ZF/Params/NB_ZF_params.m index 271b5d9..b854bd4 100644 --- a/Algorithms/Algo_Mode/Non-blind/ZF/Params/NB_ZF_params.m +++ b/Algorithms/Algo_Mode/Non-blind/ZF/Params/NB_ZF_params.m @@ -5,7 +5,7 @@ properties % Parameters num_params = 6 - params = {'No. bits', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation'} + params = {'No. samples', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation'} notations = {'N', 'Nt', 'Nr', 'ChL', 'ChType', 'Mod'} tooltips = {} % Type of the UIControl: edit_text = 1 diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Dec2Alphabet.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Dec2Alphabet.m new file mode 100644 index 0000000..35c0486 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Dec2Alphabet.m @@ -0,0 +1,15 @@ +function result = SB_EM_Dec2Alphabet( x, alphabet, M ) +% this function converts the decimal "x" into its equivalent representation +% with elements of alphabet + +P = length(alphabet); +mat = (dec2base(x-1,P,M))'; + +result = zeros(M,1); + + for i = 1:M + ind = mat(i,1); + result(i,1) = alphabet(1,str2double(ind)+1); + end +end + diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_EstimeCov.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_EstimeCov.m new file mode 100644 index 0000000..88b671d --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_EstimeCov.m @@ -0,0 +1,32 @@ +% +% script EstimeCov.m: estimation of the covariance matrix +% spatio-temporal 0 temporal lag +% Syntaxe R = EstimeCov(sig_cap,T,L,N) +% Entrees +% sig_cap = number of signal samples +% T = number of signal samples +% L = no of receivers +% N = size snapshots of each sensor ( correlation window +% Exits +% R = spatio-temporal covariance matrix (dimension LN x LN) +% +function R = SB_EM_EstimeCov(sig_cap,T,L,N) + +% +%... verification of entries +% +if (size(sig_cap) ~= [T L]) + error('Size of signals is incompatible with parameters'); +end +% +%... Calculation of covariances +% +R = zeros(L*N); +for t=1:T-N+1 + x=sig_cap(t+N-1:-1:t,:); + y=x(:); + R=R+y*y'; +end +R=(1/(T-N+1))*R; + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPred.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPred.m new file mode 100644 index 0000000..60f35fd --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPred.m @@ -0,0 +1,30 @@ +function [Trans,y] = SB_EM_FindTransitionsPred(s1,s2,M, P ) + +% find the transitions vector : x(t)=s1 and x(t+1) = s2 +% s1(t) ---> s1(t+1)=s2 s1 contains the predeccessors of s2 + +l=length(s1); +x2 =(dec2base(s2-1,P,M))'; +for i=1:l +x1(:,i) =(dec2base(s1(i)-1,P,M))'; +end + +x=[]; +for i=1:l +x=[x [x2(1);x1(:,i)]]; +end +%x = [x2(1);x1]; +for i=1:l +for j=1:M+1 + y(j,i)= str2double(x(j,i)); +end +end +y=y'; +Trans=zeros(1,l); +for i=1:l + B = sum(y(i,:).*(10.^(numel(y(i,:))-1:-1:0))); + xx = num2str(B); + Trans(1,i) = base2dec(xx,P)+1; +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPredMIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPredMIMO.m new file mode 100644 index 0000000..48a966e --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsPredMIMO.m @@ -0,0 +1,66 @@ +function [TransDec,Trans1] = SB_EM_FindTransitionsPredMIMO(s1,s2,M, P, Nt ) + +% find the transitions vector : x(t)=s1 and x(t+1) = s2 +% s1(t) ---> s1(t+1)=s2 s1 contains the predeccessors of s2 + +x2 = (dec2base(s2-1,P,Nt*M))'; %conversion of state's value to P base + +l=length(s1); +for i=1:l +x1(:,i) =(dec2base(s1(i)-1,P,Nt*M))'; +end +x21=x2(1:end/Nt); +x22=x2(end/Nt+1:end); + +for i=1:l + x11(:,i)= x1(1:end/Nt,i); + x12(:,i)= x1(end/Nt+1:end,i); +end + +for j=1:M + y21(j,1)=str2double(x21(j)); + y22(j,1)=str2double(x22(j)); +for i=1:l + y11(j,i)= str2double(x11(j,i)); + y12(j,i)= str2double(x12(j,i)); +end +end +y11=y11'; +y12=y12'; +y21=y21'; +y22=y22'; + + B = sum(y21.*(10.^(numel(y21)-1:-1:0))); + xx = num2str(B); + xx21 = base2dec(xx,P)+1; + + B = sum(y22.*(10.^(numel(y22)-1:-1:0))); + xx = num2str(B); + xx22 = base2dec(xx,P)+1; + +for i=1:l + B = sum(y11(i,:).*(10.^(numel(y11(i,:))-1:-1:0))); + xx = num2str(B); + xx11(1,i) = base2dec(xx,P)+1; + + B = sum(y12(i,:).*(10.^(numel(y12(i,:))-1:-1:0))); + xx = num2str(B); + xx12(1,i) = base2dec(xx,P)+1; +end + +[Tr1Dec,Tr1]= SB_EM_FindTransitionsPred(xx11,xx21,M, P ); +[Tr2Dec,Tr2]= SB_EM_FindTransitionsPred(xx12,xx22,M, P ); + +Trans1 = [Tr1';Tr2']; + +Trans1 = Trans1'; +TransDec = zeros(l,1); +for i=1:l +B = sum(Trans1(i,:).*(10.^(numel(Trans1(i,:))-1:-1:0))); + xx = num2str(B); + TransDec(i,1) = base2dec(xx,P)+1; +end +TransDec = TransDec'; +Trans1 = Trans1'; + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccess.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccess.m new file mode 100644 index 0000000..4694aea --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccess.m @@ -0,0 +1,30 @@ +function [Trans,y] = SB_EM_FindTransitionsSuccess(s1,s2,M, P ) + +% find the transitions vector : x(t)=s1 and x(t+1) = s2 +% s1(t) --- s1(t+1)=s2 s2 contains the successors of s1 + +l=length(s2); +x1 =(dec2base(s1-1,P,M))'; +for i=1:l +x2(:,i) =(dec2base(s2(i)-1,P,M))'; +end + +x=[]; +for i=1:l +x=[x [x2(1,i);x1]]; +end +%x = [x2(1);x1]; +for i=1:l +for j=1:M+1 + y(j,i)= str2double(x(j,i)); +end +end +y=y'; +Trans=zeros(1,l); +for i=1:l + B = sum(y(i,:).*(10.^(numel(y(i,:))-1:-1:0))); + xx = num2str(B); + Trans(1,i) = base2dec(xx,P)+1; +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccessMIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccessMIMO.m new file mode 100644 index 0000000..34d2182 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_FindTransitionsSuccessMIMO.m @@ -0,0 +1,66 @@ +function [TransDec,Trans1] = SB_EM_FindTransitionsSuccessMIMO(s1,s2,M, P, Nt ) + +% find the transitions vector : x(t)=s1 and x(t+1) = s2 +% s1(t) ---> s1(t+1)=s2 s2 contains the successors of s1 + +x1 = (dec2base(s1-1,P,Nt*M))'; %conversion of state's value to P base + +l=length(s2); +for i=1:l +x2(:,i) =(dec2base(s2(i)-1,P,Nt*M))'; +end +x11=x1(1:end/Nt); +x12=x1(end/Nt+1:end); + +for i=1:l + x21(:,i)= x2(1:end/Nt,i); + x22(:,i)= x2(end/Nt+1:end,i); +end + +for j=1:M + y11(j,1)=str2double(x11(j)); + y12(j,1)=str2double(x12(j)); +for i=1:l + y21(j,i)= str2double(x21(j,i)); + y22(j,i)= str2double(x22(j,i)); +end +end +y11=y11'; +y12=y12'; +y21=y21'; +y22=y22'; + + B = sum(y11.*(10.^(numel(y11)-1:-1:0))); + xx = num2str(B); + xx11 = base2dec(xx,P)+1; + + B = sum(y12.*(10.^(numel(y12)-1:-1:0))); + xx = num2str(B); + xx12 = base2dec(xx,P)+1; + +for i=1:l + B = sum(y21(i,:).*(10.^(numel(y21(i,:))-1:-1:0))); + xx = num2str(B); + xx21(1,i) = base2dec(xx,P)+1; + + B = sum(y22(i,:).*(10.^(numel(y22(i,:))-1:-1:0))); + xx = num2str(B); + xx22(1,i) = base2dec(xx,P)+1; +end + +[Tr1Dec,Tr1]= SB_EM_FindTransitionsSuccess(xx11,xx21,M, P ); +[Tr2Dec,Tr2]= SB_EM_FindTransitionsSuccess(xx12,xx22,M, P ); + +Trans1 = [Tr1';Tr2']; + +Trans1 = Trans1'; +TransDec = zeros(l,1); +for i=1:l + B = sum(Trans1(i,:).*(10.^(numel(Trans1(i,:))-1:-1:0))); + xx = num2str(B); + TransDec(i,1) = base2dec(xx,P)+1; +end +TransDec = TransDec'; +Trans1 = Trans1'; + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Nonlinear_MIMO_params.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Nonlinear_MIMO_params.m new file mode 100644 index 0000000..390365e --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_Nonlinear_MIMO_params.m @@ -0,0 +1,55 @@ +classdef SB_EM_Nonlinear_MIMO_params + %Params Summary of this class goes here + % Detailed explanation goes here + + properties + % Parameters + num_params = 9 + params = {'No. samples', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation', 'Threshold', 'No. pilots', 'No. iter max'} + notations = {'Ns', 'Nt', 'Nr', 'M', 'ChType', 'Mod', 'threshold', 'Np', 'N_iter_max'} + tooltips = {} + % Type of the UIControl: edit_text = 1 + % popup_menu = 2 + % button = 3 + params_type = [1, 1, 1, 1, 2, 2, 1, 1, 1] + values = {100, 2, 4, 2, {'Real', 'Complex', 'Input'}, {'Binary', 'QPSK', '4-QAM', '16-QAM', '64-QAM', '128-QAM', '256-QAM'}, 0.001, 10, 20} + default_values = {100, 2, 4, 2, 2, 3, 0.001, 10, 20} + + % Default SNR and Monte + default_Monte = 10 + default_SNR = '-10:5:20' + + % Output + % Type of the outputs: SER Sig = 1 + % BER Sig = 2 + % MSE Sig = 3 + % MSE Cha = 4 + outputs = [1, 2, 4] + default_output = 1 + + % Figure + sys_model = 'Default.png' + title = {'SB-EM Non-linear MIMO'} + xlabel = {'SNR (dB)', 'SNR (dB)', 'SNR (dB)', 'SNR (dB)'} + ylabel = {'SER', 'BER', 'MSE Signal (dB)', 'MSE Channel (dB)'} + trigger = false + position + linewidth = 1 + color = 'k' + + % Triggers/Flags + has_inter = [true, true, true, false, false, false, false, false, false] + rect = {} + rect_position = {[150 480 100 100], [260 1130 290 100], [1470 1130 290 100], 0, 0, 0, 0, 0, 0} + rect_linewidth = {2, 2, 2, 2, 2, 2, 2, 2, 2} + rect_color = {'b', 'g', 'r', 'b', 'r', 'g', 'g', 'b', 'b'} + + % Reference website + web_url = 'https://www.sciencedirect.com/science/article/abs/pii/S0165168421003340' + end + + methods (Access = private) + + end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsMIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsMIMO.m new file mode 100644 index 0000000..86b908c --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsMIMO.m @@ -0,0 +1,42 @@ +function [PredecessorsDec,tot] = SB_EM_PredecessorsMIMO( S ,P, M, Nt ) +%UNTITLED4 Summary of this function goes here +% Detailed explanation goes here + + + +x = (dec2base(S-1,P,Nt*M))'; %conversion of state's value to P base +yp = zeros(Nt*M,1); +for j = 1:Nt*M + yp(j) = str2double(x(j,1)); +end +S1 = yp(1:end/Nt,1); +S2 = yp(end/Nt+1:end,1); + + B1 = sum(S1'.*(10.^(numel(S1)-1:-1:0))); + x1 = num2str(B1); + S11= base2dec(x1,P)+1; + + B2 = sum(S2'.*(10.^(numel(S2)-1:-1:0))); + x2 = num2str(B2); + S22= base2dec(x2,P)+1; + +NumbPredc = P^Nt; +PredecessorsDec = zeros(1,NumbPredc); +[~,Pred1] = SB_EM_PredecessorsSIMO( S11 ,P, M) ; + +[~,Pred2] = SB_EM_PredecessorsSIMO( S22 ,P, M) ; +tot = []; +for i=1:P + +tot = [tot [Pred1;repmat(Pred2(:,i),1,P)]]; +end + +% conversion to decimal base +for i=1:NumbPredc + A = tot(:,i)'; + B = sum(A.*(10.^(numel(A)-1:-1:0))); + x = num2str(B); + PredecessorsDec(1,i)= base2dec(x,P)+1; +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsSIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsSIMO.m new file mode 100644 index 0000000..95f4874 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_PredecessorsSIMO.m @@ -0,0 +1,28 @@ +function [PredecessorsDec,Predecessors] = SB_EM_PredecessorsSIMO( S ,P, M ) + +% state S = [s1 s2 ... sM] = [s(t-1) s(t-2)...s(t-M)] +% predecessors of state S are of type [s2... sM x] where +% x takes all possible alphabets + +NumbPredc = P; +Predecessors = zeros(M,NumbPredc); % in P base +PredecessorsDec = zeros(1,NumbPredc); % in decimal base + +x = (dec2base(S-1,P,M))'; %conversion of state's value to P base +yp = zeros(M,1); +for j = 1:M + yp(j) = str2double(x(j,1)); +end + +for i = 1:NumbPredc + Predecessors(:,i) = [yp(2:end);i-1]; +end +% convertion to decimal base +for i=1:NumbPredc + A = Predecessors(:,i)'; + B = sum(A.*(10.^(numel(A)-1:-1:0))); + x = num2str(B); + PredecessorsDec(1,i)= base2dec(x,P)+1; +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsMIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsMIMO.m new file mode 100644 index 0000000..73e9179 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsMIMO.m @@ -0,0 +1,43 @@ +function [SuccessorsDec,tot] = SB_EM_SuccessorsMIMO( S ,P, M, Nt ) +%UNTITLED4 Summary of this function goes here +% Detailed explanation goes here + + + +x = (dec2base(S-1,P,Nt*M))'; %conversion of state's value to P base +yp = zeros(Nt*M,1); +for j = 1:Nt*M + yp(j) = str2double(x(j,1)); +end +S1 = yp(1:end/Nt,1); +S2 = yp(end/Nt+1:end,1); + + B1 = sum(S1'.*(10.^(numel(S1)-1:-1:0))); + x1 = num2str(B1); + S11= base2dec(x1,P)+1; + + B2 = sum(S2'.*(10.^(numel(S2)-1:-1:0))); + x2 = num2str(B2); + S22= base2dec(x2,P)+1; + +NumbPredc = P^Nt; +SuccessorsDec = zeros(1,NumbPredc); + +[~,Succes1] = SB_EM_SuccessorsSIMO( S11 ,P, M) ; + +[~,Succes2] = SB_EM_SuccessorsSIMO( S22 ,P, M) ; +tot = []; +for i=1:P + +tot = [tot [Succes1;repmat(Succes2(:,i),1,P)]]; +end + +% conversion to decimal base +for i=1:NumbPredc + A = tot(:,i)'; + B = sum(A.*(10.^(numel(A)-1:-1:0))); + x = num2str(B); + SuccessorsDec(1,i)= base2dec(x,P)+1; +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsSIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsSIMO.m new file mode 100644 index 0000000..2b8f0dc --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_SuccessorsSIMO.m @@ -0,0 +1,29 @@ +function [SuccessorsDec,Successors] = SB_EM_SuccessorsSIMO( S ,P, M ) + +% state S = [s1 s2 ... sM] = [s(t-1) s(t-2)...s(t-M)] +% successors of state S are of type [x s1 ... sM-1] where +% x takes all possible alphabets + +NumbSucc = P; +Successors = zeros(M,NumbSucc); % in P base +SuccessorsDec = zeros(1,NumbSucc); % in decimal base + +x = (dec2base(S-1,P,M))'; +ys = zeros(M,1); +for j = 1:M + ys(j,1) = str2double(x(j,1)); +end + +for i = 1:NumbSucc +Successors(:,i) = [i-1;ys(1:end-1)]; +end + +for i=1:NumbSucc + A = Successors(:,i)'; + B = sum(A.*(10.^(numel(A)-1:-1:0))); + x = num2str(B); + SuccessorsDec(1,i)= base2dec(x,P)+1; +end +%SuccessorsDec + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_alpha_beta2.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_alpha_beta2.m new file mode 100644 index 0000000..ef5ad65 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_alpha_beta2.m @@ -0,0 +1,44 @@ +% calcule de maniere recursive les tableaux des coefficients alpha et beta. +% +% SYNTAXE [alpha, beta]=alpha_beta(predess,success,tab,M,T) +% +% predess : c'est le tableau des predecesseurs +% success : c'est le tableau des successeurs +% tab : tableau des densites normales de probabilite +% M : degres des filtres +% T : taille d'echantillon +% +% sortie : alpha=tableau (P^M X T+1) des element alpha(i,t) +% : beta=tableau (P^M X T+1) des element beta(i,t) + +function [alpha, beta,Rho] = SB_EM_alpha_beta2(tableau,tab,M,T,Nt,P) + + +nb_etat = P^(Nt*M); +beta = zeros(nb_etat,T+1); +alpha = zeros(nb_etat,T+1); +Rho = zeros(T+1,1); +alpha(:,1) = ones(nb_etat,1)/nb_etat; +beta(:,T+1) = ones(nb_etat,1); +Rho(1) = 1; + +%%%%%%% Recursion forward +for t = 2:T+1 + for il = 1:nb_etat + itrans = find(tableau(:,2)==il); + predecessors = tableau(itrans,1); + alpha(il,t) = tab(t-1,itrans)*alpha(predecessors,t-1)/(P^Nt); + end + Rho(t) = 1/sum(alpha(:,t)); + alpha(:,t) = Rho(t)*alpha(:,t); +end + +%%%%%%% Recursion backward +for t = T:-1:1 + for il = 1:nb_etat + jtrans = find(tableau(:,1)==il); + successors = tableau(jtrans,2); + beta(il,t) = Rho(t)*tab(t,jtrans)*beta(successors,t+1)/(P^Nt); + end +end + diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_K.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_K.m new file mode 100644 index 0000000..32c1d31 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_K.m @@ -0,0 +1,35 @@ +% calcul iteratif de l'inverse de la matrice K = sigma*I+sum_{k} h_k*h_k^{T} + +function K_1 = SB_EM_cal_K(sigma,can,T) + +%%%% degres des filtres +M = length(can(1,:))-1; +%%%% nombre de canaux +L = length(can(:,1)); +h=can(:); +K_1 = (1/sigma)*eye(T*L); + + +for ind=1:M + k=h(L*(M+1-ind)+1:L*(M+1)); + il = 1:ind*L; + x=K_1(:,il)*k; + K_1 = K_1 - (x/(1+k'*x(il)))*x'; +end + + +for ind=M+1:T + il = (ind-M-1)*L+1:ind*L; + x=K_1(:,il)*h; + K_1 = K_1 - (x/(1+h'*x(il)))*x'; +end; + +for ind=T+1:T+M + k = h(1:(M+1+T-ind)*L); + il = (ind-M-1)*L+1:L*T; + x=K_1(:,il)*k; + K_1 = K_1 - (x/(1+k'*x(il)))*x'; +end; + + + diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_frac.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_frac.m new file mode 100644 index 0000000..d77b286 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_frac.m @@ -0,0 +1,18 @@ +% calcul de la fraction rationnelle f^(-1)fi'f^(-1)fj' +% +% SYNTAXE : [b,a]=cal_frac(sigma,canaux,ipapam,jparam); +% +% entrees : +% sigma: la puissance du bruit +% canaux: tableau des coefficients des filtres des differents canaux +% iparam,jparam: indices des parametres consideres +% +% sorties : +% b: coefficients du polynome au numerateur +% a: coefficients du polynome au denominateur + +function [b,a] = SB_EM_cal_frac(sigma,canaux,ipapam,jparam); + +L = length(canaux(:,1)); %L : taille de l'antenne +M = length(canaux(1,:))-1; %M : degre des filtres pour chaque canaux +nb_param=2*L*(M+1)-1; %nb_param : nombre de parametres a estimer \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_param.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_param.m new file mode 100644 index 0000000..cd7754b --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_param.m @@ -0,0 +1,42 @@ +% calcul des reestimees de la matrice H et de la puissance de bruit sigma +% +% SYNTAXE [sigma,H]=cal_param(proba,sig_cap,trans) +% +% prob_red : tableau des plus forte densites de probabilites L(Y,Xt=En) +% sig_cap : signaux capteurs +% trans : tableau ((M+1) X P^(M+1)) des transitions +% selec_trans : tableau des transitions selectionnees (celles de plus forte proba) +% M : degres des filtres +% P : taille de l'alphabet +% +% H : la reestimee de la matrice de filtrage +% sigma : la reestimee de la puissance du bruit + +function [sigma,H] = SB_EM_cal_param(prob_red,sig_cap,trans,selec_trans) + + +L=min(size(sig_cap)); +M=min(size(trans))-1; +[nbtr,T]=size(selec_trans); +% +%... vraisemblance approximee de l'observation pour les parametres courants +% +Likelihood= sum(prob_red(:,1)); + +Rxx=zeros(M+1); +Rxy=zeros(M+1,L); +Ryy=zeros(L); + +Ryy=Likelihood*sig_cap.'*conj(sig_cap); +for t=1:T + slice=selec_trans(:,t); + Rxx=Rxx+trans(:,slice)*diag(prob_red(:,t))*trans(:,slice)'; + for icap=1:L + x=trans(:,slice)*diag(prob_red(:,t))*conj(sig_cap(t,L)); + Rxy(:,icap)=Rxy(:,icap)+sum(x.').'; + end +end +Ryx=Rxy'; + +H=Ryx*inv(Rxx); +sigma=trace(Ryy-H*Rxy)/(Likelihood*T*L); diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_parametres.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_parametres.m new file mode 100644 index 0000000..ef887b0 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_parametres.m @@ -0,0 +1,30 @@ +% calcul des expressions +% +% alpha_ij-------->tableau alpha , i< j<=i+M +% beta_ij --------> tableau beta , i< j<=i+M +% gamma_ij-------->tableau gamma , i< j<=i+M +% rho_ij ---------> tableau rho , i< j<=i+M +% lambda_ij------->tableau lambda , i< j<=i+M +% +% SYNTAXE [alpha,beta,gamma,rho,lambda]=cal_parametres(tab1,tab2,tab3); +% +% tab1 : tableau des hj'*Ki^(-1)hj , i<=j<=i+M +% tab2 : tableau des hi'*Ki^(-1)hj , i<=j<=i+M +% tab3 : tableau des hj'*Ki^(-1)y , i<=j<=i+M + +function [alpha,beta,gamma,rho,lambda] = SB_EM_cal_parametres(tab1,tab2,tab3) + +[dim1,dim2] = size(tab1); % dim1=T+M +M=dim2-1; + +for ind1=1:dim1 + for ind2=ind1+1:min(ind1+M,dim1) + ind = ind2-ind1; + alpha(ind1,ind) = tab1(ind1,1)+abs(tab2(ind1,ind+1))^2/(1-tab1(ind1,ind+1)); + beta(ind1,ind) = tab1(ind1,ind+1)/(1-tab1(ind1,ind+1)); + gamma(ind1,ind) = tab2(ind1,ind+1)/(1-tab1(ind1,ind+1)); + rho(ind1,ind) = tab3(ind1,1)+tab2(ind1,ind+1)*tab3(ind1,ind+1)/ ... + (1-tab1(ind1,ind+1)); + lambda(ind1,ind) = tab3(ind1,ind+1)/(1-tab1(ind1,ind+1)) ; + end; +end; diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_prob2.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_prob2.m new file mode 100644 index 0000000..21cae32 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_prob2.m @@ -0,0 +1,21 @@ +% calcul les densites de probabilite L(Y,Xt=En) +% +% SYNTAXE proba=cal_prob(alpha,beta,tab,P,M,T) +% +% alpha,beta : les tableaux des coefficients alpha et beta. +% tab : tableau des densites normale de proba (aux points (Yt-HEn)) +% M : degres des filtres +% P : taille de l'alphabet +% T : taille de l'echantillon +% +% proba : c'est un tableau (P^(M+1) X T) contenant toutes les densites de +% probabilites. + +function proba = SB_EM_cal_prob2(alpha,beta,tab,tableau,P,M,T,Nt) + +nb_tran = P^(Nt*(M+1)); + +for itrans = 1:nb_tran + proba(itrans,:) = (alpha(tableau(itrans,1),1:T).*beta(tableau(itrans,2),2:T+1).*tab(:,itrans).')/(P^(Nt)); +end + diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_B.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_B.m new file mode 100644 index 0000000..b2b1245 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_B.m @@ -0,0 +1,59 @@ +% calcul des reestimees de la matrice H et de la puissance de bruit sigma +% +% SYNTAXE [sigma,H]=cal_sigmaH(proba,sig_cap,trans) +% +% proba : tableau des densites de probabilites L(Y,Xt=En) +% sig_cap : signaux capteurs +% trans : tableau ((M+1) X P^(M+1)) des transitions +% M : degres des filtres +% P : taille de l'alphabet +% +% H : la reestimee de la matrice de filtrage +% sigma : la reestimee de la puissance du bruit + +function [sigma,H] = SB_EM_cal_sigmaH_B(proba,sig_cap,trans,Nt) + +T = max(size(sig_cap)); +L = min(size(sig_cap)); +M = min(size(trans))/(Nt*2)-1; +nb_trans = max(size(trans)); +%Np = size(pilots,2); + +%% ...normalization des probabilités + for i=1:size(proba,2) + proba(:,i)=proba(:,i)/sum(proba(:,i)); + end + +%... vraisemblance de l'observation pour les parametres courants +% +Likelihood = sum(proba(:,1)); +coeff = sum(proba.'); + +Rxx = zeros(Nt*2*(M+1)); +Rxy = zeros(Nt*2*(M+1),L); +Ryy = zeros(L); + +Ryy = Likelihood*sig_cap.'*conj(sig_cap); +%Rxx=trans*diag(sum(proba.'))*trans'; +for n = 1:nb_trans + Rxx = Rxx+coeff(n)*trans(:,n)*trans(:,n)'; + x = trans(:,n); + for icap = 1:L, + D(icap,:) = sig_cap(:,icap).'.*proba(n,:); + end + Rxy = Rxy + x*sum(D'); +end +Ryx = Rxy'; +fact = sum(proba(:,1))*L; +%% ... pilots +%Rxx_pilot = zeros(Nt*2*(M+1)); +%Ryx_pilot = zeros(L,Nt*2*(M+1)); +%Np = size(pilots,2); +%for i=1:Np +% Rxx_pilot = Rxx_pilot + pilots(:,i)*pilots(:,i)'; +% Ryx_pilot = Ryx_pilot + sigcap_pilot(:,i)*pilots(:,i)'; +%end +%% ... +H = (Ryx)*pinv(Rxx); + +sigma = trace(Ryy-H*Rxy)/fact/T; diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_SB.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_SB.m new file mode 100644 index 0000000..2edea33 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_sigmaH_SB.m @@ -0,0 +1,61 @@ +% calcul des reestimees de la matrice H et de la puissance de bruit sigma +% +% SYNTAXE [sigma,H]=cal_sigmaH(proba,sig_cap,trans) +% +% proba : tableau des densites de probabilites L(Y,Xt=En) +% sig_cap : signaux capteurs +% trans : tableau ((M+1) X P^(M+1)) des transitions +% M : degres des filtres +% P : taille de l'alphabet +% +% H : la reestimee de la matrice de filtrage +% sigma : la reestimee de la puissance du bruit + +function [sigma,H] = SB_EM_cal_sigmaH_SB(proba,sig_cap,trans,sigcap_pilot,pilots,Nt) + +T = max(size(sig_cap)); +L = min(size(sig_cap)); +M = min(size(trans))/(Nt*2)-1; +nb_trans = max(size(trans)); +Np = size(pilots,2); + +%% ...normalization des probabilités +for i=1:size(proba,2) + proba(:,i)=proba(:,i)/sum(proba(:,i)); +end + +%... vraisemblance de l'observation pour les parametres courants +% +Likelihood = sum(proba(:,1)); +coeff = sum(proba.'); + +Rxx = zeros(Nt*2*(M+1)); +Rxy = zeros(Nt*2*(M+1),L); +Ryy = zeros(L); + +Ryy = Likelihood*sig_cap.'*conj(sig_cap); +%Rxx=trans*diag(sum(proba.'))*trans'; +for n = 1:nb_trans + Rxx = Rxx+coeff(n)*trans(:,n)*trans(:,n)'; + x = trans(:,n); + for icap = 1:L, + D(icap,:) = sig_cap(:,icap).'.*proba(n,:); + end + Rxy = Rxy + x*sum(D'); +end +Ryx = Rxy'; +fact = sum(proba(:,1))*L; +%% ... pilots +Rxx_pilot = zeros(Nt*2*(M+1)); +Ryx_pilot = zeros(L,Nt*2*(M+1)); +Np = size(pilots,2); +for i=1:Np + Rxx_pilot = Rxx_pilot + pilots(:,i)*pilots(:,i)'; + Ryx_pilot = Ryx_pilot + sigcap_pilot(:,i)*pilots(:,i)'; +end +%% ... +H = (Ryx+Ryx_pilot)*pinv(Rxx+Rxx_pilot); + +sigma = trace(Ryy-H*Rxy)/fact/T; + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tab.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tab.m new file mode 100644 index 0000000..74511ce --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tab.m @@ -0,0 +1,29 @@ +% calcul et stock les densites de proba gaussienne aux points (Yt_HEn) +% +% SYNTAXE tab=cal_tab(sig_cap,trans,H,sigma) +% +% sig_cap : tableau des signaux recus par les capteurs +% trans : tableau ((M+1) X P^(M+1)) des transitions +% H : la matrice de filtrage +% sigma : la puissance du bruit blanc +% +% sortie : tab=tableau (T X taille) de toutes les densites de proba +% taille : nbr de transitions +% T : taille de l'echantillon + +function tab = SB_EM_cal_tab(sig_cap,trans,H,sigma) +% +%... calcul du nombre d'observations T et du nombre de capterus L +% +T = max(size(sig_cap)); +L = min(size(sig_cap)); +A = zeros(L,T); +taille = max(size(trans)); +Htrans = H*trans; +for itrans = 1:taille + for icap = 1:L, + A(icap,:) = sig_cap(:,icap).'-Htrans(icap,itrans); + end + tab(:,itrans) = real((1/pi/sigma)^L*exp(- sum(A.*conj(A),1).'/sigma)); +end +clear A diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tableaux.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tableaux.m new file mode 100644 index 0000000..cb8dd1a --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_cal_tableaux.m @@ -0,0 +1,67 @@ +% Calcul des expressions : +% +% hj'*Ki^(-1)hj , i<=j<=i+M -------> tab1 +% +% hi'*Ki^(-1)hj , i<=j<=i+M -------> tab2 +% +% hj'*Ki^(-1)y , i<=j<=i+M -------> tab3 +% +% SYNTAXE [tab1,tak2,tab3] = cal_tableaux(sigma,can,K_1,sig_cap); +% +% can : estimee initiale des coefficients des canaux +% sigma : estimmee initiale de la puissance du bruit +% K_1 : inverse de la matrice K = sigma*I+sum_{k} h_k*h_k^{T} +% sig_cap : tableau des signaux recus par les capteurs + +function [tab1,tab2,tab3] = SB_EM_cal_tableaux(sigma,can,K_1,sig_cap) + +%%%% degres des filtres +M = length(can(1,:))-1; +%%%% nombre de canaux +L = length(can(:,1)); +%%%% Nombre d'echantillons +T = length(sig_cap(:,1)); +h = can(:); +tmp = sig_cap.'; +y = tmp (:); + + +for ind1 = 1 : T+M +%%%% calcul de h_i + if (ind1<=M), + hi = h(L*(M+1-ind1)+1:L*(M+1)); + il = 1:ind1*L; + elseif(M seuil) && (comptnew_vrais) + error('The likelihood is diminishing!!!') + end + + old_vrais = new_vrais; + [estsigma1,estcan1] = SB_EM_cal_sigmaH_SB(proba,sig_cap,trans,sigcap_pilot,pilots,Nt); + + vec_new = reshape(estcan1.',Ncap*Nt*2*(M+1),1); + vec_old = reshape(estcan.',Ncap*Nt*2*(M+1),1); + + test = norm(vec_new-vec_old)/norm(vec_old); %+abs(estsigma1-estsigma); + + estcan = estcan1; + estsigma1 = estsigma; + +end +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_min_symbol_errorMIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_min_symbol_errorMIMO.m new file mode 100644 index 0000000..94dd024 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_min_symbol_errorMIMO.m @@ -0,0 +1,38 @@ +% detection des symboles d'information +% +% SYNTAXE estsig_source= min_symbol_error(proba,alphabet,M) +% +%% +% Entree +% proba : tableau (P^(M+1) X T) contenant toutes la vraisemblance conditionelle +% des observations connaissant les transitions a l'instant t +% M : degres des filtres +% P : taille de l'alphabet +% T : taille de l'echantillon +% alphabet : vecteur complexe des valeurs effectives de l'alphabet +% + +function estsig_source= SB_EM_min_symbol_errorMIMO(proba,alphabet,M,Nt) + +P=max(size(alphabet)); +nb_etat=P^(Nt*(M+1)-1); +T=max(size(proba(1,:))); + + +symb_prob=zeros(P,T); +estsig_source=zeros(T,1); +% +%%%% calculate of L(Y,At=alpha) +% +for isymb=1:P + symb_prob(isymb,:)=sum(proba((isymb-1)*nb_etat+1:isymb*nb_etat,:)); +end + + +[~, b] = max(symb_prob); +clear a symb_prob ; +for it=1:T + estsig_source(it)=alphabet(b(it)); +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_predecesseur.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_predecesseur.m new file mode 100644 index 0000000..7b60fee --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_predecesseur.m @@ -0,0 +1,23 @@ +% Calcule le tableau des predecesseurs associes a chaque etat +% +% SYNTAXE predess = predecesseur(P,M) +% +% P : est la taille de l'alphabet +% M : est la dimension du vecteur d'etat +% +% sortie : predess est un tableau (P^M X P) tq a chaque etat (indice ligne) +% on lui associe ses predecesseurs (indices colonnes) + +function predess = SB_EM_predecesseur(P,M,Nt) + +taille=P^(Nt*M); +x=P^(Nt*(M-1)); +predess=zeros(taille,P^Nt); +for il=1:taille + for ic=1:P^Nt + y=il-1-fix((il-1)/x)*x; + predess(il,ic)=y*(P^Nt)+ic; + end +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_successeur.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_successeur.m new file mode 100644 index 0000000..b3be592 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_successeur.m @@ -0,0 +1,22 @@ +% Calcule le tableau des successeurs associes a chaque etat +% +% SYNTAXE success = successeur(P,M) +% +% P : est la taille de l'alphabet +% M : est la dimension du vecteur d'etat +% +% sortie : success est un tableau (P^M X P) tq a chaque etat (indice ligne) +% on lui associe ses successeurs (indices colonnes) + +function success = SB_EM_successeur(P,M,Nt) + +taille=P^(Nt*M); +x=P^(Nt*(M-1)); +success=zeros(taille,P^Nt); +for il=1:taille + for ic=1:P^Nt + success(il,ic)=fix((il-1)/(P^Nt))+(ic-1)*x+1; + end +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_transitions.m b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_transitions.m new file mode 100644 index 0000000..5db5ee6 --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/Params/SB_EM_transitions.m @@ -0,0 +1,12 @@ +function mat = SB_EM_transitions(alphabet, dim ) + +P = length(alphabet); +taille = P^dim; +mat = zeros(dim,taille); +for il = 1:dim + for x = 1:P^il + mat(il,(x-1)*P^(dim-il)+1:x*P^(dim-il))=alphabet(mod(x-1,P)+1); + end +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/EM/SB_EM_Nonlinear_MIMO.m b/Algorithms/Algo_Mode/Semi-blind/EM/SB_EM_Nonlinear_MIMO.m new file mode 100644 index 0000000..b570e5e --- /dev/null +++ b/Algorithms/Algo_Mode/Semi-blind/EM/SB_EM_Nonlinear_MIMO.m @@ -0,0 +1,182 @@ +function Err = B_EM_Nonlinear_MIMO(Op, SNR_i, Output_type) + +%% Blind Expectation-Maximization for Non-linear MIMO communications +% +%% Input: + % + 1. Ns: number of sample data + % + 2. Nt: number of transmit antennas + % + 3. Nr: number of receive antennas + % + 4. M: length of the channel + % + 5. Ch_type: type of the channel (real, complex, + % user' input + % + 6. Mod_type: type of modulation (All) + % + 7. Threshold: threshold of EM algorithm + % + 8. Np: number of pilot symbols + % + 9. N_iter_max: number of maximum iterations EM + % + 10. SNR_i: signal noise ratio + % + 11. Output_type: SER / BER / MSE Channel +% +%% Output: + % + 1. Err: SER / BER / MSE Channel +% +%% Algorithm: + % Step 1: Initialize variables + % Step 2: Return +% +% Ref: Ouahbi Rekik, Karim Abed-Meraim, Mohamed Nait-Meziane, +% Anissa Mokraoui, and Nguyen Linh Trung, "Maximum likelihood +% based identification for nonlinear multichannel communications +% systems," Signal Processing, vol. 189, p. 108297, Dec. 2021. +% +%% Require R2006A + +% Author: Ouahbi Rekik, L2TI, UR 3043, Université Sorbonne Paris Nord, France + +% Adapted for InSI by Do Hai Son, 1-Aug-2023 +% InSI: A MatLab Toolbox for Informed System Identification in +% Wireless Communications +% https://avitech-vnu.github.io/InSI +% Project: NAFOSTED 01/2019/TN on Informed System Identification +% PI: Nguyen Linh Trung, Vietnam National University, Hanoi, Vietnam +% Co-PI: Karim Abed-Meraim, Université d’Orléans, France + + +% Initialize variables +Ns = Op{1}; % number of sig sequences +Nt = Op{2}; % number of transmit antennas +Nr = Op{3}; % number of receive antennas +M = Op{4}; % length of the channel +Ch_type = Op{5}; % complex +Mod_type = Op{6}; +threshold = Op{7}; % threshold of EM algorithm +Np = Op{8}; % number of pilot symbols +N_iter_max= Op{9}; % number of maximum iterations EM +Np_init = Np; + + +% Generate input signal +modulation = {'Bin', 'QPSK', 'QAM4', 'QAM16', 'QAM64', 'QAM128', 'QAM256'}; + +% Generate channel +if Ch_type == 2 + channels_mat = Generate_channel(Nr, Nt * 2*(M+1) - 1, Ch_type) * sqrt(2); +else + channels_mat = Generate_channel(Nr, Nt * 2*(M+1) - 1, Ch_type); +end +channels_vec = reshape(channels_mat, [], 1); % vectorize channel matrix + +% Generate signals +[sig_src, data] = eval(strcat(modulation{Mod_type}, '(1000)')); +alphabet = unique(sig_src); +P = length(alphabet); +average_energy_per_symb = norm(alphabet)^2/P; % or: 2/3*(P-1); +alphabet = alphabet/sqrt(average_energy_per_symb); % normalization + +%% transitions, predecessors, successors +trans1 = SB_EM_cal_trans(alphabet, Nt*(M+1)); +trans2 = trans1.^2; % quadratic model +trans = [trans1; trans2]; +nbr_etats = P^(Nt*M); %number of states +successors = zeros(nbr_etats,P^Nt); +predecessors = zeros(nbr_etats,P^Nt); +tableau = zeros(P^(Nt*(M+1)),2); % tableau(:,1)-->predecesseurs (i) ,tableau(:,2)-->successeurs (j) + +for i=1:nbr_etats + predecessors(i,:) = SB_EM_PredecessorsMIMO( i ,P, M, Nt ); + [itransPred,~] = SB_EM_FindTransitionsPredMIMO(predecessors(i,:),i,M, P, Nt ); + tableau(itransPred,2) = i; + + successors(i,:) = SB_EM_SuccessorsMIMO( i ,P, M, Nt ); + [itransSuccess,~] = SB_EM_FindTransitionsSuccessMIMO(i,successors(i,:),M, P, Nt ); + tableau(itransSuccess,1) = i; +end + +%% Transmitted signal +S = []; +D = []; + +for i = 1:Nt + [sig_src, data] = eval(strcat(modulation{Mod_type}, '(Ns + M)')); + S = [S; sig_src.']; + D = [D; data.']; +end +S_decim = D; +S = S/sqrt(average_energy_per_symb); + +Stop1 = toeplitz(S(1,M+1:-1:1),S(1,M+1:Ns+M)); +D_Stop1 = toeplitz(S_decim(1,M+1:-1:1),S_decim(1,M+1:Ns+M)); + +if Nt==1 + S_mat = [Stop1; Stop1.^2]; + + D_mat = [D_Stop1; D_Stop1.^2]; +else + Stop2 = toeplitz(S(2,M+1:-1:1),S(2,M+1:Ns+M)); + S_mat = [Stop1; Stop2; Stop1.^2; Stop2.^2]; + + D_Stop2 = toeplitz(S_decim(2,M+1:-1:1), S_decim(2,M+1:Ns+M)); + D_mat = [D_Stop1; D_Stop2; D_Stop1.^2; D_Stop2.^2]; +end + +%% Received signal +sig_cap_sans_bruit = channels_mat*S_mat; + +% ... pilots +Sp = []; +Dp = []; + +for i = 1:Nt + [sig_src, data] = eval(strcat(modulation{Mod_type}, '(Ns + M)')); + Sp = [Sp; sig_src.']; + Dp = [Dp; data.']; +end +Sp_decim = Dp; +Sp = Sp/sqrt(average_energy_per_symb); + +Stop1p = toeplitz(Sp(1,M+1:-1:1),Sp(1,M+1:Np+M)); +if Nt==1 + Sp_mat = [Stop1p; Stop1p.^2]; +else + Stop2p = toeplitz(Sp(2,M+1:-1:1), Sp(2,M+1:Np+M)); + Sp_mat = [Stop1p; Stop2p; Stop1p.^2; Stop2p.^2]; +end +sigp_cap_sans_bruit = channels_mat*Sp_mat; + +% --- received signal strength ---------------------------------- +Pr = norm(sig_cap_sans_bruit,'fro').^2/numel(sig_cap_sans_bruit); + +sigmav2 = 10^((10*log10(Pr)-SNR_i)/10); % noise variance for received signal + +noise = sqrt(sigmav2)*(randn(Nr,Ns) + randn(Nr,Ns)*1i)/sqrt(2); + +sig_cap = sig_cap_sans_bruit + noise; +sigcap_pilot = sigp_cap_sans_bruit + noise(:,1:Np); %noisep; + +%----------------- pilot-based initialization ------------------- +canauxp = sig_cap(:,1:Np_init)*pinv(S_mat(:,1:Np_init)); + +%-------------------------- SB EM ------------------------------- +[probaB, ~, channels_mat_EM_SB, ~] = ... + SB_EM_func(sig_cap.', trans, sigcap_pilot, Sp_mat, canauxp, sigmav2, ... + predecessors, successors, tableau, N_iter_max, threshold,Nt); % SB-EM + +%% Test NaN values +if any(isnan(channels_mat_EM_SB)) + channels_mat_EM_SB = canauxp; + compteur_sb(SNRidx,1) = compteur_sb(SNRidx,1)+1; +end + +%% Vectorize estimated channels +channels_vec_EM_SB = reshape(channels_mat_EM_SB, [],1); % h_{EM-SB} semi-blind + +%% Data detection +est_src = SB_EM_min_symbol_errorMIMO(probaB,alphabet,M,Nt); + +% Compute SER / BER / MSE channel +if Output_type ~= 4 + Err = ER_func(D_mat(1,:).', est_src, Mod_type, Output_type); +else + Err = ER_func(channels_vec.', channels_vec_EM_SB, Mod_type, Output_type); +end + +end \ No newline at end of file diff --git a/Algorithms/Algo_Mode/Semi-blind/MRE/Params/SB_MRE_rc_params.m b/Algorithms/Algo_Mode/Semi-blind/MRE/Params/SB_MRE_rc_params.m index c7bef79..8fef767 100644 --- a/Algorithms/Algo_Mode/Semi-blind/MRE/Params/SB_MRE_rc_params.m +++ b/Algorithms/Algo_Mode/Semi-blind/MRE/Params/SB_MRE_rc_params.m @@ -5,8 +5,8 @@ properties % Parameters num_params = 9 - params = {'No. bits', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation', 'Window length', 'No. pilots', 'Blind ratio'} - notations = {'N', 'Nt', 'Nr', 'ChL', 'ChType', 'Mod', 'L', 'N_p', 'lambda'} + params = {'No. samples', 'No. transmitters', 'No. receivers', 'Channel order', 'Channel type', 'Modulation', 'Window length', 'No. pilots', 'Blind ratio'} + notations = {'Ns', 'T', 'L', 'M', 'ChType', 'Mod', 'N', 'Np', 'lambda'} tooltips = {} % Type 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index 74d1dd0..ee99529 100644 --- a/Shared/General/ER_func.m +++ b/Shared/General/ER_func.m @@ -50,7 +50,7 @@ case 1 % SER switch (called_file(1:index-1)) case 'B' - if (Mod_type ~= 1) + if (Mod_type ~= 1 && exist('sig_src', 'var')) est_src = est_src' * sig_src * est_src; % remove the inherent scalar indeterminacy related to the blind processing end case 'SB' @@ -79,7 +79,7 @@ case 2 % BER switch (called_file(1:index-1)) case 'B' - if (Mod_type ~= 1) + if (Mod_type ~= 1 && exist('sig_src', 'var')) est_src = est_src' * sig_src * est_src; % remove the inherent scalar indeterminacy related to the blind processing end case 'SB'