BER for BPSK through frequency selective fading channel using rayleighchan function and rls algorithm for adaptive equalizer

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zainab altaii 2013-7-27

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回答： Sophiya 2025-3-18

please help me with my project I need to measure the performance evaluation of BPSK through a three path frequency selective fading channel... I search in the help browser of the matlab and I found the following code but it doesn't work correctly.. so if you please help me and I will very thankful clear

clc

close all

k=0;

for EsNodB=27:5:27

s1=RandStream('mrg32k3a','Seed',0);

s4=RandStream('mrg32k3a','Seed',1);

s5=RandStream('mrg32k3a','Seed',2);

errcount=0;

Tsym = 1e-6; % Symbol period (s)

bitsPerSymbol = 1; % Number of bits per BPSK symbol

M = 2.^bitsPerSymbol; % BPSK alphabet size (number of modulation levels)

nPayload = 40; % Number of payload symbols

nTrain = 10; % Number of training symbols

nTail = 20; % Number of tail symbols

hStream = RandStream.create('mt19937ar', 'seed', 12345);

hStream1 = RandStream.create('mt19937ar', 'seed', 12354);

for i=1:1:1

pskmodObj = modem.pskmod(M); % modulator object

xTextSym=randi(s1, [0 M-1], 1, nPayload)

xText = modulate(pskmodObj, xTextSym)

% Training sequence symbols

xTrainSym = randi(hStream, [0 M-1], 1, nTrain);

% Modulated training sequence

xTrain = modulate(pskmodObj, xTrainSym);

% Tail sequence symbols

xTailSym = randi(hStream1, [0 M-1], 1, nTail);

% Modulated tail sequence

xTail = modulate(pskmodObj, xTailSym);

nSymFilt = 8; % Number of symbol periods spanned by each filter

osfFilt = 4; % Oversampling factor for filter (samples per symbol)

rolloff =0.25; % Rolloff factor

Tsamp = Tsym/osfFilt; % TX signal sample period (s)

orderFilt = nSymFilt*osfFilt; % Filter order (number of taps - 1) % Filter responses and structures

hTxFilt = fdesign.interpolator(osfFilt, 'Square Root Raised Cosine', ... osfFilt, 'N,Beta', orderFilt, rolloff)

hRxFilt = fdesign.decimator(osfFilt, 'Square Root Raised Cosine', ... osfFilt, 'N,Beta', orderFilt, rolloff)

hDTxFilt = design(hTxFilt)

hDRxFilt = design(hRxFilt)

hDTxFilt.PersistentMemory = true; hDRxFilt.PersistentMemory = true;

% Multipath channel

fd = 0; % Maximum Doppler shift (Hz)

chan = rayleighchan(Tsamp, fd); % Create channel object.

chan.PathDelays = [0 0.4 0.9 ]*Tsym; % Path delay vector (s)

chan.AvgPathGaindB = [0 -15 -20]; % Average path gain vector (dB)

chan.ResetBeforeFiltering = 0; % Allow state retention across blocks.

delay = chan.ChannelFilterDelay

%simName = 'Linear equalization for frequency-selective fading';

ri=filter(chan,xText)

rx=filter(chan,xTrain)

snrdB = EsNodB - 10*log10(osfFilt); % Signal-to-noise ratio per sample (dB)

%n = randn(s4,1,nPayload) + 1i*randn(s5,1,nPayload); % white gaussian noise, 0dB variance

n = randn(s4,1,nPayload); % white gaussian noise, 0dB variance

std=sqrt(10^(-snrdB/10));
ni=std*n;

%ri1=ri+ni;

% Adaptive equalizer

nWeights = 8;

forgetFactor = 0.99; % RLS algorithm forgetting factor

alg = rls(forgetFactor); % RLS algorithm object

eqObj = lineareq(nWeights, alg) % Equalizer object

eqObj.RefTap = 3; % Reference tap

y = equalize(eqObj,ri,rx)

%disp('y');disp(y);

pskdmodObj = modem.pskdemod(M);

y1 = demodulate(pskdmodObj, y)

%y1 = real(y)>0;

disp('y1');disp(y1);

%nErr = size(find(xTextSym- y1),2);

%for j=1:1:100 % if y1(j)~=xTextSym(j) % errcount=errcount+1; % end % end

%errcount=errcount+nErr;

%reset(pskmodObj); %reset(pskdmodObj); % Compute bit error rate, taking delay into account. % Truncate to account for channel delay.

xTextSym_trunc = xTextSym(1:end-delay); y1_trunc = y1(delay+1:end);

[num,ber] = biterr(xTextSym_trunc,y1_trunc) % Bit error rate

errcount=errcount+ber;

end

k=k+1;

%BER(k)=ber

BER(k)=(errcount/1)

theoryBerAWGN(k) = 0.5*erfc(sqrt(10^(snrdB/10))) % theoretical ber

%EbN0Lin = 10^(snrdB/10);

%theoryBer(k) = 0.5*(1-sqrt(EbN0Lin/(EbN0Lin+1)))

theoryBer(k) = berfading(snrdB,'dpsk',M,1)

snr(k) =snrdB

end

figure

%semilogy(snr,theoryBerAWGN,'cd-','LineWidth',2);

%hold on

semilogy(snr,theoryBer,'bp-','LineWidth',2);

hold on

semilogy(snr,BER,'mx-','LineWidth',2);

grid on

legend('Rayleigh-Theory', 'Rayleigh-Simulation');

%legend('AWGN-Theory','Rayleigh-Theory', 'Rayleigh-Simulation');

xlabel('Eb/No, dB');

ylabel('Bit Error Rate');

title('BER for BPSK modulation in Rayleigh channel');

grid on

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ramya sri 2017-8-11

I want coding for multipath channel plz help me

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Answer 1

shaik 2014-1-19

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/83348-ber-for-bpsk-through-frequency-selective-fading-channel-using-rayleighchan-function-and-rls-algorith#answer_121338

clc

close all

k=40;

for EsNodB=1:k

s1=RandStream('mrg32k3a','Seed',0);

s4=RandStream('mrg32k3a','Seed',1);

s5=RandStream('mrg32k3a','Seed',2);

errcount=0;

Tsym = 1e-6; % Symbol period (s)

bitsPerSymbol = 1; % Number of bits per BPSK symbol

M = 2.^bitsPerSymbol; % BPSK alphabet size (number of modulation levels)

nPayload = 40; % Number of payload symbols

nTrain = 10; % Number of training symbols

nTail = 20; % Number of tail symbols

hStream = RandStream.create('mt19937ar', 'seed', 12345);

hStream1 = RandStream.create('mt19937ar', 'seed', 12354);

for i=1:1:1

pskmodObj = modem.pskmod(M); % modulator object

xTextSym=randi(s1, [0 M-1], 1, nPayload)

xText = modulate(pskmodObj, xTextSym)

% Training sequence symbols

xTrainSym = randi(hStream, [0 M-1], 1, nTrain);

% Modulated training sequence

xTrain = modulate(pskmodObj, xTrainSym);

% Tail sequence symbols

xTailSym = randi(hStream1, [0 M-1], 1, nTail);

% Modulated tail sequence

xTail = modulate(pskmodObj, xTailSym);

nSymFilt = 8; % Number of symbol periods spanned by each filter

osfFilt = 4; % Oversampling factor for filter (samples per symbol)

rolloff =0.25; % Rolloff factor

Tsamp = Tsym/osfFilt; % TX signal sample period (s)

orderFilt = nSymFilt*osfFilt; % Filter order (number of taps - 1) % Filter responses and structures

hTxFilt = fdesign.interpolator(osfFilt, 'Square Root Raised Cosine', osfFilt, 'N,Beta', orderFilt, rolloff)

hRxFilt = fdesign.decimator(osfFilt, 'Square Root Raised Cosine',osfFilt, 'N,Beta', orderFilt, rolloff)

hDTxFilt = design(hTxFilt)

hDRxFilt = design(hRxFilt)

hDTxFilt.PersistentMemory = true; hDRxFilt.PersistentMemory = true;

% Multipath channel

fd = 0; % Maximum Doppler shift (Hz)

chan = rayleighchan(Tsamp, fd); % Create channel object.

chan.PathDelays = [0 0.4 0.9 ]*Tsym; % Path delay vector (s)

chan.AvgPathGaindB = [0 -15 -20]; % Average path gain vector (dB)

chan.ResetBeforeFiltering = 0; % Allow state retention across blocks.

delay = chan.ChannelFilterDelay

%simName = 'Linear equalization for frequency-selective fading';

ri=filter(chan,xText)

rx=filter(chan,xTrain)

snrdB = EsNodB - 10*log10(osfFilt); % Signal-to-noise ratio per sample (dB)

n = randn(s4,1,nPayload) + 1i*randn(s5,1,nPayload); % white gaussian noise, 0dB variance

n = randn(s4,1,nPayload); % white gaussian noise, 0dB variance

std=sqrt(10^(-snrdB/10));

ni=std*n;

ri1=ri+ni;

% Adaptive equalizer

nWeights = 8;

forgetFactor = 0.99; % RLS algorithm forgetting factor

alg = rls(forgetFactor); % RLS algorithm object

eqObj = lineareq(nWeights, alg) % Equalizer object

eqObj.RefTap = 3; % Reference tap

y = equalize(eqObj,ri,rx)

disp('y');disp(y);

pskdmodObj = modem.pskdemod(M);

y1 = demodulate(pskdmodObj, y)

y1 = real(y)>0;

disp('y1');disp(y1);

nErr = size(find(xTextSym- y1),2);

for j=1:1:40 if y1(j)~=xTextSym(j) errcount=errcount+1; end end

errcount=errcount+nErr;

reset(pskmodObj); reset(pskdmodObj); % Compute bit error rate, taking delay into account. % Truncate to account for channel delay.

xTextSym_trunc = xTextSym(1:end-delay); y1_trunc = y1(delay+1:end);

[num,ber] = biterr(xTextSym_trunc,y1_trunc) % Bit error rate

errcount=errcount+ber;

end

k=k+1;

BER(k)=ber

BER(k)=(errcount/1)

theoryBerAWGN(k) = 0.5*erfc(sqrt(10^(snrdB/10))) % theoretical ber

EbN0Lin = 10^(snrdB/10);

theoryBer(k) = 0.5*(1-sqrt(EbN0Lin/(EbN0Lin+1)))

theoryBer(k) = berfading(snrdB,'dpsk',M,1)

snr(k) =snrdB

end snr figure

semilogy(snr,theoryBerAWGN,'*','LineWidth',2);

hold on

semilogy(snr,theoryBer,'g-*','LineWidth',2);

hold on

semilogy(snr,BER,'r-*','LineWidth',2);

grid on

legend('Rayleigh-Theory', 'Rayleigh-Simulation');

legend('AWGN-Theory','Rayleigh-Theory', 'Rayleigh-Simulation');

xlabel('Eb/No, dB');

ylabel('Bit Error Rate');

title('BER for BPSK modulation in Rayleigh channel');

grid on

this is the coding whose error has been removed

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CHRISTOPHER NWAOGU 2016-9-24

Line 7 For EsNodB=1:k there is a problem in this line please help

student student 2018-12-10

I'm trying to obtain the frequency response of rayleigh fading channel that i designed in Matlab. This is the function used to get rayleigh fading channel (rayleighchan), any help.

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Answer 2

Sophiya 2025-3-18

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/83348-ber-for-bpsk-through-frequency-selective-fading-channel-using-rayleighchan-function-and-rls-algorith#answer_1561964

clc;

clear;

close all;

% Simulation Parameters

N = 1e6; % Number of bits

EbN0_dB = 0:2:20; % SNR range in dB

BER_rayleigh = zeros(size(EbN0_dB));

% Generate random binary data (0s and 1s)

bits = randi([0 1], 1, N);

% BPSK Modulation (0 -> -1, 1 -> +1)

bpsk_mod = 2*bits - 1;

% Rayleigh fading channel

h = (randn(1, N) + 1j*randn(1, N)) / sqrt(2); % Complex fading (Rayleigh)

for i = 1:length(EbN0_dB)

% Convert Eb/N0 from dB to linear scale

EbN0 = 10^(EbN0_dB(i) / 10);

noise_variance = 1 / (2*EbN0);

% Apply Rayleigh fading

faded_signal = h .* bpsk_mod;

% Add AWGN noise

noise = sqrt(noise_variance) * (randn(1, N) + 1j*randn(1, N));

received_signal = faded_signal + noise;

% Coherent Detection (assuming perfect channel estimation)

received_equalized = received_signal ./ h;

% BPSK Demodulation

bits_received = real(received_equalized) > 0;

% Compute Bit Error Rate (BER)

BER_rayleigh(i) = sum(bits ~= bits_received) / N;

end

% Theoretical BER for BPSK in Rayleigh fading

theoretical_BER_rayleigh = 0.5 * (1 - sqrt(EbN0_dB ./ (EbN0_dB + 1)));

% Plot Results

figure;

semilogy(EbN0_dB, BER_rayleigh, 'ro-', 'LineWidth', 2);

hold on;

semilogy(EbN0_dB, theoretical_BER_rayleigh, 'b--', 'LineWidth', 2);

grid on;

xlabel('Eb/N0 (dB)');

ylabel('Bit Error Rate (BER)');

legend('Simulated BER (Rayleigh)', 'Theoretical BER (Rayleigh)');

title('BPSK Performance in Rayleigh Fading Channel');

hold off;clear;close all;% Simulation ParametersN = 1e6; % Number of bitsEbN0_dB = 0:2:20; % SNR range in dBBER_rayleigh = zeros(size(EbN0_dB));% Generate random binary data (0s and 1s)bits = randi([0 1], 1, N); % BPSK Modulation (0 -> -1, 1 -> +1)bpsk_mod = 2*bits - 1;% Rayleigh fading channelh = (randn(1, N) + 1j*randn(1, N)) / sqrt(2); % Complex fading (Rayleigh)for i = 1:length(EbN0_dB) % Convert Eb/N0 from dB to linear scale EbN0 = 10^(EbN0_dB(i) / 10); noise_variance = 1 / (2*EbN0); % Apply Rayleigh fading faded_signal = h .* bpsk_mod; % Add AWGN noise noise = sqrt(noise_variance) * (randn(1, N) + 1j*randn(1, N)); received_signal = faded_signal + noise; % Coherent Detection (assuming perfect channel estimation) received_equalized = received_signal ./ h; % BPSK Demodulation bits_received = real(received_equalized) > 0; % Compute Bit Error Rate (BER) BER_rayleigh(i) = sum(bits ~= bits_received) / N;end% Theoretical BER for BPSK in Rayleigh fadingtheoretical_BER_rayleigh = 0.5 * (1 - sqrt(EbN0_dB ./ (EbN0_dB + 1)));% Plot Resultsfigure;semilogy(EbN0_dB, BER_rayleigh, 'ro-', 'LineWidth', 2);hold on;semilogy(EbN0_dB, theoretical_BER_rayleigh, 'b--', 'LineWidth', 2);grid on;xlabel('Eb/N0 (dB)');ylabel('Bit Error Rate (BER)');legend('Simulated BER (Rayleigh)', 'Theoretical BER (Rayleigh)');title('BPSK Performance in Rayleigh Fading Channel');hold off;

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BER for BPSK through frequency selective fading channel using rayleighchan function and rls algorithm for adaptive equalizer

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BER for BPSK through frequency selective fading channel using rayleighchan function and rls algorithm for adaptive equalizer

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