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tester_det_dec_beams_SCFDMA_MU.m
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function [RMSE_CE, MSE_detector, FER] = tester_det_dec_beams_SCFDMA_MU(scenario, estimator_DMRS, estimator_SRS, ML_coef1, channels)
% Same as in demo to work in training mode
N_user=length(scenario.UE_power); % number of all users in scenario
scenario.N_user = N_user;
% If channels are not passed to function - load
if ~exist("channels",'var') || ~isfield(channels,'h_srs')
seed = 100*scenario.seed+10000*scenario.index;
rng(seed);
[h_pilot, h_data, h_srs,~] = get_channel(scenario); % read channel
else
h_pilot = channels.h_pilot;
h_data = channels.h_data;
h_srs = channels.h_srs;
end
Nrx = scenario.Nrx; % N-of-Rx antennas
comb = scenario.comb;
SC_FDMA = scenario.SC_FDMA;
% RB_size = 12 subcarriers
RB_size=scenario.RB_size;
RB_num=scenario.RB_num;
RB_num_Ruu=scenario.RB_num_Ruu;
% N-of-subcarriers (max=600)
N_used=RB_num*RB_size; % var - use it to change simulation set
% N of symbols per TTI
% N_ofdm = 14;
N_data_sym=scenario.N_data_sym;
% N-of-pilot-symbols
N_pilot_sym = 2;
% Pilot positions in TTI
pilot_positions=[4 11];
% Constelation order
QAM_order=scenario.QAM_order;
QAM_points=2^QAM_order;
SNR = scenario.SNR;
N_target_UE = scenario.N_target_UE; % number of target users in scenario
% N_scenarios=scenario.N_scenarios;
ZC_root=scenario.ZC_root;
ZC_shift=scenario.ZC_shift;
% initialize LDPC
% switch scenario.code_block_index
% case 2
% matrix_name='LDPC_matrix_72_288';
% code_rate=0.75;
% code_length=288;
% case 1
% matrix_name='LDPC_matrix_1152_2304';
% code_rate=0.5;
% code_length=2304;
% case 0
% matrix_name='LDPC_matrix_144_288';
% code_rate=0.5;
% code_length=288;
% end
K_LDPC = 144;
code_length=288;
code_rate = K_LDPC/code_length;
P = [ 36 39 39 0 -1 -1
44 37 -1 -1 0 -1
3 14 22 -1 -1 0 ];
blockSize = 48;
pcmatrix = ldpcQuasiCyclicMatrix(blockSize,P);
cfgLDPCEnc = ldpcEncoderConfig(pcmatrix);
cfgLDPCDec = ldpcDecoderConfig(pcmatrix);
% HS = load(matrix_name).HS;
% hLDPCenc = comm.LDPCEncoder(HS);
% init_ldpc = @(x) decode_soft(0, x);
% [H_LDPC, ~] = alist2sparse([matrix_name '.alist']);
% [temp, N_LDPC] = size(H_LDPC);
% K_LDPC = N_LDPC - temp;
% [ldpc, ~, ~] = init_ldpc([matrix_name '.alist']);
% scale_array = 0.75*ones(1, N_LDPC);
% offset_array = 0*ones(1,N_LDPC);
% calculate default averaged power (from the file)
UE_power_default = zeros(1,N_user);
for i=1:N_user
UE_power_default(i)=mean( mean( mean( squeeze(h_pilot(i,:,:,:)).*conj(squeeze(h_pilot(i,:,:,:))) )));
end
% generate white noise for pilots
noise_p = (randn(Nrx, N_used, N_pilot_sym)+1i*randn(Nrx, N_used, N_pilot_sym)) / sqrt(2);
% generate white noise for data symbols
noise_d = (randn(Nrx, N_used, N_data_sym)+1i*randn(Nrx, N_used, N_data_sym)) / sqrt(2);
% estimate noise power per subcarrier
noise_power = sum(sum(sum(abs(noise_p).*abs(noise_p))))/(Nrx*N_used*N_pilot_sym); % - algorithm is required !!!!!
h_pilot_noisy=noise_p;
h_pilot_noisy_splitted(1:N_user, :, :, :) = repmat(permute(noise_p,[4,1,2,3]), [N_user, 1,1,1]);
h_data_noisy=noise_d;
v=0 : N_used-1;
data_uncoded = zeros(N_user, N_used*QAM_order*N_data_sym / code_length, K_LDPC);
data_coded = zeros(N_used*QAM_order*N_data_sym / code_length, code_length);
s_tx_f = zeros(N_user, N_used, N_data_sym);
for i=1:N_user
ZC_vec = circshift( exp(-1i*(pi*ZC_root(i)*v.*(v+1)) / N_used) , [0 ZC_shift(i)] );
ZC_array = repmat(ZC_vec,Nrx,N_pilot_sym);
ZC_array = reshape(ZC_array,Nrx,N_used,2);
if i<=N_target_UE
tmp_SNR=SNR;
else
tmp_SNR=0; % ATTENTION! CHANGED FROM "0"!!!!
end
gain = sqrt(10^( (scenario.UE_power(i)+tmp_SNR) / 10)) / sqrt(UE_power_default(i));
h_pilot(i,:,:,:) = gain*squeeze(h_pilot(i,:,:,:));
h_data(i,:,:,:) = gain*squeeze(h_data(i,:,:,:));
for j=1:N_used*QAM_order*N_data_sym / code_length
data_uncoded(i,j,:) = randi([0 1], K_LDPC, 1);
tmp=squeeze(data_uncoded(i,j,:));
data_coded(j,:) = ldpcEncode(tmp,cfgLDPCEnc);
end
% Modulate data
dataInMatrix = reshape(data_coded,[],QAM_order); % Reshape data into binary k-tuples, k = log2(M)
dataSymbolsIn = bi2de(dataInMatrix); % Convert to integers
tmp=qammod(dataSymbolsIn.',QAM_points,'UnitAveragePower', true); % Gray coding, phase offset = 0;
s_tx = reshape( tmp , N_used, [] );
% generate channel response of single UE with noise
data_mod = zeros(Nrx, N_used, N_data_sym);
for k=1:N_data_sym
if SC_FDMA
s_tx_f(i,:,k)=fft(s_tx(:,k))/sqrt(N_used);
else
s_tx_f(i,:,k)=s_tx(:,k);
end
for m=1:N_used
data_mod(:,m,k)=squeeze(h_data(i,:,m,k))*s_tx_f(i,m,k);
end
end
h_pilot_noisy=h_pilot_noisy+squeeze(h_pilot(i,:,:,:)).*ZC_array.* reshape(scenario.OC_code(i,:), [1,1,N_pilot_sym]);
h_pilot_noisy_splitted(i,:,:,:) = squeeze(h_pilot_noisy_splitted(i,:,:,:)) + ...
squeeze(h_pilot(i,:,:,:)).*ZC_array.* reshape(scenario.OC_code(i,:), [1,1,N_pilot_sym]);
% test interf impact on CE
if i == N_target_UE
h_pilot_noisy_target_UE_only = h_pilot_noisy;
end
h_data_noisy=h_data_noisy+data_mod;
end
% dummy scaling for CE unit (depends on pilots scaling in comb mode)
h_pilot_noisy=h_pilot_noisy/sqrt(1+comb);
h_pilot_noisy_target_UE_only=h_pilot_noisy_target_UE_only/sqrt(1+comb);
h_pilot_noisy_splitted = h_pilot_noisy_splitted / sqrt(1+comb);
%LS CE calculation for DMRS
h_pilot_LS = zeros(N_target_UE, Nrx, N_used, N_pilot_sym);
for i=1:N_target_UE
ZC_vec = circshift( exp(-1i*(pi*ZC_root(i)*v.*(v+1)) / N_used) , [0 ZC_shift(i)] );
ZC_array = repmat(ZC_vec,Nrx,N_pilot_sym);
ZC_array = reshape(ZC_array,Nrx,N_used,2);
if scenario.consider_interf_on_CE == 1
noisy_pilot = h_pilot_noisy;
else
noisy_pilot = h_pilot_noisy_target_UE_only;
end
if scenario.ideal_user_split
h_pilot_LS(i,:,:,:)=squeeze(h_pilot_noisy_splitted(i,:,:,:)).*conj(ZC_array).* reshape(scenario.OC_code(i,:), [1,1,N_pilot_sym]);
else
h_pilot_LS(i,:,:,:)=noisy_pilot.*conj(ZC_array).* reshape(scenario.OC_code(i,:), [1,1,N_pilot_sym]);
end
end
% DMRS params
DMRS_Params.SNR_dummy=SNR;
DMRS_Params.RB_size = RB_size;
DMRS_Params.RB_num = RB_num;
DMRS_Params.N_pilot = N_pilot_sym;
DMRS_Params.pilot_positions=pilot_positions;
DMRS_Params.comb=comb;
DMRS_Params.Nrx = scenario.Nrx;
DMRS_Params.N_ports = scenario.N_ports;
DMRS_Params.beam_transform = scenario.beam_transform;
DMRS_Params.comb_split = scenario.comb_split;
DMRS_Params.OC_code = scenario.OC_code;
DMRS_Params.interp_order = scenario.interp_order;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SRS params
SRS_Params=DMRS_Params;
SRS_Params.N_ports = scenario.N_ports;
SRS_Params.RB_num=16;
N_ports=scenario.N_ports;
gain_SRS=sqrt(2); % SRS power is higher then DMRS
if ~scenario.ideal_SRS
comb_SRS=0;
N_used_SRS=SRS_Params.RB_num*RB_size;
% generate white noise for SRS
h_srs_noisy = (randn(Nrx, N_used_SRS, N_pilot_sym)+1i*randn(Nrx, N_used_SRS, N_pilot_sym)) / sqrt(2);
v=0 : N_used_SRS-1;
for i=1:N_user
ZC_vec = circshift( exp(-1i*(pi*ZC_root(i)*v.*(v+1)) / N_used_SRS) , [0 ZC_shift(i)] );
ZC_array = repmat(ZC_vec,Nrx,N_pilot_sym);
ZC_array = reshape(ZC_array,Nrx,N_used_SRS,2);
if i<=N_target_UE
tmp_SNR=SNR;
else
tmp_SNR=0;
end
gain = sqrt(10^( (scenario.UE_power(i)+tmp_SNR) / 10)) / sqrt(UE_power_default(i));
h_srs(i,:,:,:) = gain_SRS*gain*squeeze(h_srs(i,:,:,:));
h_srs_noisy=h_srs_noisy+squeeze(h_srs(i,:,:,:)).*ZC_array;
end
% dummy (depends on pilots scaling in comb mode)
h_srs_noisy=h_srs_noisy/sqrt(1+comb_SRS);
% LS CE for calculation SRS
h_srs_MU = zeros(N_user, Nrx, N_used_SRS);
for i=1:N_user
ZC_vec = circshift( exp(-1i*(pi*ZC_root(i)*v.*(v+1)) / N_used_SRS) , [0 ZC_shift(i)] );
ZC_array = repmat(ZC_vec,Nrx,N_pilot_sym);
ZC_array = reshape(ZC_array,Nrx,N_used_SRS,2);
h_srs_LS = h_srs_noisy.* conj(ZC_array);
% Beam angles estimation via SRS (100 TTIs delay between SRS and current DMRS)
[~,~,h_srs_f]=estimator_SRS(h_srs_LS,SRS_Params,ML_coef1);
h_srs_MU(i,:,:)=h_srs_f;
end
[SRS_transform_matrix]=beamspace_selection(h_srs_MU, scenario);
else
gain = sqrt(10.^( (scenario.UE_power+tmp_SNR) / 10)) ./ sqrt(UE_power_default);
h_srs = gain_SRS.*reshape(gain, [N_user,1,1,1]).*h_srs;
h_srs = mean(h_srs, 4);
% default beamspace selection algorithm has power equalization
if 0
[SRS_transform_matrix]=beamspace_selection_new(h_srs, scenario);
else
[SRS_transform_matrix]=beamspace_selection(h_srs, scenario);
end
end
% Channel transfer to the beam domain
h_beam_noisy = zeros(N_used, N_ports, N_pilot_sym);
h_beam_LS = zeros(N_target_UE, N_used, N_ports, N_pilot_sym);
for j=1:N_pilot_sym
h_beam_noisy(:,:,j)=squeeze(h_pilot_noisy(:,:,j)).'*conj(SRS_transform_matrix);
for i=1:N_target_UE
% 64 antennas -> N_ports
h_beam_LS(i,:,:,j)=squeeze(h_pilot_LS(i,:,:,j)).'*conj(SRS_transform_matrix);
end
end
% specify input data for Channel Estimation unit
IN_DATA.SRS_transform_matrix=SRS_transform_matrix;
scenario.SRS_transform_matrix=SRS_transform_matrix;
transform_matrix=SRS_transform_matrix;
% Initialize both to pass into some functions
beam_amplitudes = zeros(N_target_UE, N_used, N_ports, N_data_sym);
h_data_recovered_f = zeros(N_target_UE, Nrx, N_used, N_data_sym);
RMSE_CE(1:N_target_UE) = 0;
if scenario.ideal_CE
% ideal CE
h_data_recovered_f = h_data(1:N_target_UE,:,:,:);
for j=1:N_data_sym
for i=1:N_target_UE
% 64 antennas -> N_ports
beam_amplitudes(i,:,:,j)=squeeze(h_data(i,:,:,j)).'*conj(SRS_transform_matrix);
end
end
%transform_matrix=eye(Nrx);
SNR_CE=Nrx*RB_num*(10^(SNR/10));
else
beam_amplitudes_ideal = zeros(size(beam_amplitudes));
for i=1:N_target_UE
% channel estimation
IN_DATA.h_f_noisy=squeeze(h_pilot_LS(i,:,:,:));
IN_DATA.h_beam_noisy=squeeze(h_beam_LS(i,:,:,:));
IN_DATA.user_ind = i;
CE_DATA = estimator_DMRS(IN_DATA, DMRS_Params, ML_coef1);
if i==1
SNR_CE=CE_DATA.SNR;
end
switch scenario.beam_transform
case 1
beam_amplitudes(i,:,:,:)=CE_DATA.SRS_beam_amplitudes;
for j = 1:N_data_sym
beam_amplitudes_ideal(1,:,:,j)=squeeze(h_data(i,:,:,j)).'*conj(SRS_transform_matrix);
end
RMSE_CE(i) = sum(abs(beam_amplitudes(i,:,:,:) - beam_amplitudes_ideal(1,:,:,:)).^2,'all') / sum(abs(beam_amplitudes_ideal).^2,'all');
case 0
h_data_recovered_f(i,:,:,:)=CE_DATA.h_data_recovered_f;
RMSE_CE(i) = sum(abs(h_data_recovered_f(i,:,:,:) - h_data(1:N_target_UE,:,:,:)).^2,'all') / sum(abs(h_data(1:N_target_UE,:,:,:)).^2,'all');
end
end
end
% MMSE Weight vector calculation
err_data=zeros(1,N_target_UE);
% error calculation
switch scenario.beam_transform
case 1
dim=N_ports;
pilot_noisy=permute(h_beam_noisy, [2, 1, 3]);
pilot_recovered=permute(beam_amplitudes, [1, 3, 2, 4]);
case 0
dim=Nrx;
pilot_noisy=h_pilot_noisy;
pilot_recovered=h_data_recovered_f;
end
pilot_recovered = pilot_recovered(:,:,:,1:N_pilot_sym);
pilot_noisy = pilot_noisy * sqrt (1+comb);
if comb
for i=1:N_target_UE
comb_matrix=ones(1, dim,N_used,N_pilot_sym);
if scenario.comb_split
first_ind = mod(i,2)+1;
else
first_ind = 2;
end
comb_matrix(1,:,first_ind:2:N_used,:)=zeros(1,dim,N_used/2,N_pilot_sym);
pilot_recovered(i,:,:,:)=sqrt(2)*pilot_recovered(i,:,:,:).*comb_matrix;
end
end
v=0 : N_used-1;
% s_rx_f = zeros(N_target_UE, RB_num_Ruu*RB_size, N_data_sym);
s_rx_f = zeros(N_target_UE, N_used, N_data_sym);
m=0;
for Ruu_index=1:round( N_used / (RB_num_Ruu*RB_size) )
U = zeros(RB_num_Ruu*RB_size, N_pilot_sym, dim);
Ruu=zeros(dim,dim);
for i=1:N_pilot_sym
for j=1:RB_num_Ruu*RB_size
m=m+1;
sc_index = RB_num_Ruu*RB_size*(Ruu_index-1)+j;
if scenario.ideal_Ruu == 1
if N_target_UE == N_user % if there is no interference
Ruu=noise_power*eye(dim);
else
Ruu=Ruu+noise_power*eye(dim);
u= zeros(dim,1);
for k=N_target_UE+1:N_user
ZC = circshift( exp(-1i*(pi*ZC_root(k)*v.*(v+1)) / N_used) , [0 ZC_shift(k)] );
CE = squeeze(h_pilot(k,:,sc_index,i)).';
if scenario.beam_transform == 1
CE = SRS_transform_matrix' * CE;
end
u=u + CE * ZC(sc_index)*scenario.OC_code(k,i);
end
end
else
u= squeeze(pilot_noisy(:,sc_index,i));
for k=1:N_target_UE
ZC = circshift( exp(-1i*(pi*ZC_root(k)*v.*(v+1)) / N_used) , [0 ZC_shift(k)] );
CE = squeeze(pilot_recovered(k,:,sc_index,i)).';
u=u - CE * ZC(sc_index)*scenario.OC_code(k,i);
end
end
U(j,i,:) = u;
Ruu=Ruu+u*u';
end
end
U = reshape(U, [RB_num_Ruu*RB_size*N_pilot_sym, dim]).';
alpha = single(scenario.alpha) * single(noise_power); %ATTENTION
U = single(U);
if scenario.alpha ~= 0
Ruu = U * U' / ( N_pilot_sym*RB_num_Ruu*RB_size ) + alpha * eye(dim);
else
Ruu = diag(repmat(mean(diag(U * U' / ( N_pilot_sym*RB_num_Ruu*RB_size ) )),[dim 1]));
end
for k=1:N_data_sym
w = zeros(RB_num_Ruu*RB_size, N_target_UE, dim);
for j=1:RB_num_Ruu*RB_size
m=RB_num_Ruu*RB_size*(Ruu_index-1)+j;
[~,H] = get_data_for_detector(m, k, beam_amplitudes, h_data_recovered_f, h_data_noisy, transform_matrix, scenario);
x = [1:scenario.interp_step:(RB_num_Ruu*RB_size-1) RB_num_Ruu*RB_size];
if ismember(j,x)
R = chol(Ruu);
H = R'\H;
R2 = chol(H'*H + single(eye(N_target_UE)));
w(j, :, :) = R2\(R2'\(H'))/R';
end
end
if scenario.interp_step > 1
for i = 1 : N_target_UE
for j = 1 : dim
w(:, i, j) = interp1(x, w(x, i, j), 1:RB_num_Ruu*RB_size);
end
end
end
% Code for single precision or double precision detector
for j=1:RB_num_Ruu*RB_size
m=RB_num_Ruu*RB_size*(Ruu_index-1)+j;
[Y,~] = get_data_for_detector(m, k, beam_amplitudes, h_data_recovered_f, h_data_noisy, transform_matrix, scenario);
w_eq = reshape(w(j, :, :), [N_target_UE, dim]);
s_rx_f(:,m,k) = w_eq*Y;
end
end
end
err_data=sum(abs(s_rx_f-s_tx_f(1:N_target_UE, :,:)).^2, [2,3]);
MSE_detector=err_data/(N_used*N_data_sym);
SNR_CE=max(10^-5, SNR_CE);
BER = ones(1,N_target_UE);
FER = ones(1,N_target_UE);
for i=1:N_target_UE
if SC_FDMA
s_rx=ifft(squeeze(s_rx_f(i,:,:)))*sqrt(N_used);
else
s_rx=squeeze(s_rx_f(i,:,:));
end
LLR_points = qamdemod(s_rx,QAM_points,'UnitAveragePower',true,'OutputType', 'approxllr', 'NoiseVariance', 1/SNR_CE);
LLR_array = flip(permute( reshape (LLR_points, QAM_order, 1,[]), [3,2,1]), 3);
LLR_vec = reshape( LLR_array, [], code_length);
frame_err=0;
data_decoded = zeros(QAM_order*N_data_sym*N_used/code_length, K_LDPC);
for j=1:QAM_order*N_data_sym*N_used/code_length
data_decoded(j,:) = ldpcDecode(squeeze(LLR_vec(j,:).'),cfgLDPCDec,20);
frame_err=frame_err+(sum(squeeze(data_decoded(j,:))~=squeeze(data_uncoded(i,j,:)).')>0);
end
BER(i) = sum(sum(data_decoded~=squeeze(data_uncoded(i,:,:))))/(N_used*N_data_sym*QAM_order*code_rate);
FER(i)= frame_err/(QAM_order*N_data_sym*N_used/code_length);
end