How to modify modem.qammod for the latest MATLAB version?

clc ;
clear all ;
close all ;
m =512; % Total number of OFDM symbols
N =1024; % Length of each OFDM symbol
M =4; % Size of the Constellation ( M can be 4 , 8 , 16 ,32 , 64 , 128 , 256)

pilotFrequency =8 ; % Pilot Symbol insertion frequency
E =2; % Energy of each Pilot Symbol
Ncp =256 ; % Length of the Cyclic Prefix
Tx = modem.qammod (‘M’,M ) ; % Choosing the modulation format as M – ary Quadrature Amplitude Modulation (M- QAM )
Rx = modem.qamdemod (‘M’,M) ; % Fixing up the demodulation format at the receiving end as M – ary QAM

% ACO – OFDM Transmitter
Data = randi ([0 M-1] ,m , N ); % Generation of Random bits Matrix of size m by N
for k1 =1: m
for m1 =1: N
if mod ( m1 ,2) ==0 % Performing Modulo operation to extract the even subcarriers
Data ( k1 , m1 ) =0; % Setting the Even Subcarriers to Zero
end
end
end

DataMod = modulate ( Tx , Data ) ; % Performing Data Modulation
DataMod_serialtoparallel = DataMod .’; % Performing Serial to Parallel Conversion
PLoc = 1: pilotFrequency :N ; % Fixing the location of Pilot carrires
DLoc = setxor (1: N , PLoc ) ; % Fixing the location of Data subcarriers
DataMod_serialtoparallel ( PLoc ,:) = E*DataMod_serialtoparallel ( PLoc ,:) ; % Inserting Pilot carriers

datamat = DataMod_serialtoparallel ; % Assigning the total data including the pilots to a variable called datamat
% Computation of Hermitian Symmetry Criteria
datamat (1 ,:) =0; % Assigning the First subcarrier to Zero
datamat (513 ,:) =0; % Assigning the Middle Subcarrier to Zero
datamat (514:1024 ,:) = flipud ( conj ( datamat (2:512 ,:) )) ; % Illustrating that only half of the subcarriers are exploited for data transmission as the remaining half are flipped complex conjugate versions of the previous ones .

d_ifft = ifft (( datamat ) ) ; % Computation of IFFT operation

% Ensuring that only the positive portion of the signal is transmitted
for k2 =1: N
for m2 =1: m
if( d_ifft ( k2 , m2 ) <0)
d_ifft ( k2 , m2 ) =0;
end
end
end

d_ifft_paralleltoserial = d_ifft .’; % Parallel to Serial Conversion
CP_part = d_ifft_paralleltoserial (: ,end – Ncp +1: end) ; %Addition of Cyclic Prefix
ACOOFDM_CP =[ CP_part d_ifft_paralleltoserial ]; %Transmissin of ACO – OFDM signal

% VLC Channel Modeling
theta = 70; %LED semi – angle
ml = – log10 (2) / log10 ( cos ( theta ) ) ; % Computation of Lambertian Mode Number
APD =0.01; % Area of the photodiode
lx =5; ly =5; lz =3; % Size of the Dimensions of the Indoor Room Environment
h =2.15;
[ XT , YT ]= meshgrid ([ – lx /4 lx /4] ,[ – ly /4 ly /4]) ;
Nx = lx *5; Ny = ly *5;
x = linspace ( – lx /2 , lx /2 , Nx );
y = linspace ( – ly /2 , ly /2 , Ny );
[ XR , YR ]= meshgrid (x , y );

D1 = sqrt (( XR – XT (1 ,1) ) .^2+( YR – YT (1 ,1) ) .^2+ h ^2) ;
D2 = sqrt (( XR – XT (2 ,2) ) .^2+( YR – YT (2 ,2) ) .^2+ h ^2) ;
cosphi_A1 = h ./ D1 ;
receiver_angle = acosd ( cosphi_A1 ) ;

H_A1 =3600*(( ml +1) * APD .* cosphi_A1 .^( ml +1) ./(2* pi .* D1.^2) +( ml +1) * APD .* cosphi_A1 .^( ml +1) ./(2* pi .^2* D1.^2* D2 .^2) ) ; % Computation of VLC channel Impulse Response taking into consideration Non Line of Sight ( NLOS ) Environment
H_A2 = H_A1 ./ norm ( H_A1 );

d_channell = filter ( H_A2 (1 ,1:2) ,1 , ACOOFDM_CP .’) .’; %Illustration of channel effect on the transmitted ACO – OFDM signal

count =0;
snr_vector =0:1:30; % size of signal to noise ratio (SNR ) vector

for snr = snr_vector
SNR = snr + 10* log10 ( log2 (M ) ) ;
count = count +1 ;
ACOOFDM_with_chann = awgn(d_channell , SNR ,’measured’) ; % Addition of AWGN
% Receiver of ACO – OFDM
ACOOFDM_removal_CP = ACOOFDM_with_chann (: , Ncp+1: N + Ncp );
% Removal of Cyclic Prefix
ACOOFDM_serialtoparallel = ACOOFDM_removal_CP .’; %Serial to Parallel Conversion
ACOOFDM_parallel_fft = fft ( ACOOFDM_serialtoparallel) ;
% Computation of FFT operation
% Channel Estimation
TransmittedPilots = DataMod_serialtoparallel ( PLoc,:) ; % Extracting the transmitted pilots
ReceivedPilots = ACOOFDM_parallel_fft ( PLoc ,:) ;
%Extracting the received pilot tones effected by channel
for r =1:m
H_MMSE(: , r ) = MMSE(ReceivedPilots(: , r ) ,TransmittedPilots(: , r ), N, pilotFrequency, H_A2(1 ,1:2), SNR);% Minimum Mean SquareError ( MMSE ) Channel Estimation
end

HData_MMSE_parallel1 = H_MMSE.’;
ACOOFDM_SERIAL_MMSE = demodulate(Rx ,(ACOOFDM_parallel_fft.’)./( HData_MMSE_parallel1) ) ;

% Recovery of Pilots from the Original Transmitted signal and Received Signal
Data_no_pilots = Data (: , DLoc ) ;
Recovered_Pilot_MMSE = ACOOFDM_SERIAL_MMSE (: , DLoc ) ;
% Computation of Bit Error Rate
[~ , recoveredMMSE( count ) ]= biterr ( Data_no_pilots (: ,2:255) , Recovered_Pilot_MMSE (: ,2:255) ) ;
end
% Plotting the BER curves
semilogy ( snr_vector , recoveredMMSE ,’gs -‘,’LineWidth’,2) ;
axis ([0 30 10^ -4 1]) ;
grid on ;

function H_MMSE = MMSE(RxP,TxP,N,pilotFrequency,h_CIR,SNR)
% I modified the MMSE_CE function provided in :MIMO-OFDM Wireless
% Communications with MATLAB¢ç Yong Soo Cho, Jaekwon Kim,
% Won Young Yang and Chung G. Kang
%2010 John Wiley & Sons (Asia) Pte Ltd
% The modification made the function more suitable with my OFDM Matlab code
noiseVar = 10^(SNR*0.1);
Np=N/pilotFrequency; % Number of Pilots
H_LS = RxP./TxP; % LS estimate
k=0:length(h_CIR)-1;
hh = h_CIR*h_CIR’;
% tmp = h_CIR.*conj(h_CIR).*k;
r = sum(h_CIR.*conj(h_CIR).*k)/hh;
r2 = (h_CIR.*conj(h_CIR).*k)*k.’/hh;
t_rms = sqrt(r2-r^2); % rms delay
D = 1j*2*pi*t_rms/N; % Denomerator of Eq. (6.16) page 192
K1 = repmat([0:N-1].’,1,Np);
K2 = repmat([0:Np-1],N,1);
rf = 1./(1+D*(K1-K2*pilotFrequency));
K3 = repmat([0:Np-1].’,1,Np);
K4 = repmat([0:Np-1],Np,1);
rf2 = 1./(1+D*pilotFrequency*(K3-K4));
Rhp = rf;
Rpp = rf2 + eye(length(H_LS),length(H_LS))/noiseVar;
H_MMSE = transpose(Rhp*inv(Rpp)*H_LS); % MMSE channel estimate

endclc ;
clear all ;
close all ;
m =512; % Total number of OFDM symbols
N =1024; % Length of each OFDM symbol
M =4; % Size of the Constellation ( M can be 4 , 8 , 16 ,32 , 64 , 128 , 256)

d_ifft = ifft (( datamat ) ) ; % Computation of IFFT operation

% Ensuring that only the positive portion of the signal is transmitted
for k2 =1: N
for m2 =1: m
if( d_ifft ( k2 , m2 ) <0)
d_ifft ( k2 , m2 ) =0;
end
end
end

d_channell = filter ( H_A2 (1 ,1:2) ,1 , ACOOFDM_CP .’) .’; %Illustration of channel effect on the transmitted ACO – OFDM signal

count =0;
snr_vector =0:1:30; % size of signal to noise ratio (SNR ) vector

HData_MMSE_parallel1 = H_MMSE.’;
ACOOFDM_SERIAL_MMSE = demodulate(Rx ,(ACOOFDM_parallel_fft.’)./( HData_MMSE_parallel1) ) ;

end clc ;
clear all ;
close all ;
m =512; % Total number of OFDM symbols
N =1024; % Length of each OFDM symbol
M =4; % Size of the Constellation ( M can be 4 , 8 , 16 ,32 , 64 , 128 , 256)

d_ifft = ifft (( datamat ) ) ; % Computation of IFFT operation

% Ensuring that only the positive portion of the signal is transmitted
for k2 =1: N
for m2 =1: m
if( d_ifft ( k2 , m2 ) <0)
d_ifft ( k2 , m2 ) =0;
end
end
end

d_channell = filter ( H_A2 (1 ,1:2) ,1 , ACOOFDM_CP .’) .’; %Illustration of channel effect on the transmitted ACO – OFDM signal

count =0;
snr_vector =0:1:30; % size of signal to noise ratio (SNR ) vector

HData_MMSE_parallel1 = H_MMSE.’;
ACOOFDM_SERIAL_MMSE = demodulate(Rx ,(ACOOFDM_parallel_fft.’)./( HData_MMSE_parallel1) ) ;

end matlab, ofdm, channel estimation, visible light communication, ber, snr, modem.qammod, qammod MATLAB Answers — New Questions

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