ofdm_fang_zhen.m

在OFDM仿真模型的基础上用MATLAB语言编写出OFDM发送、信道及接收整个系统,在系统仿真正确的前提下,对循环前缀、信道估计等性能改善方法进行进一步仿真验证,得到了预期的理想结果

clear all;
close all;
fprintf('OFDM仿真 \n\n');
IFFT_bin_length = 1024;
carrier_count = 200;
symbols_per_carrier = 50;
bits_per_symbol = 2;
SNR = input('SNR = ');
baseband_out_length = carrier_count*symbols_per_carrier*bits_per_symbol;
carriers = (1:carrier_count) + (floor(IFFT_bin_length/4)-floor(carrier_count/2));
conjugate_carriers = IFFT_bin_length - carriers + 2;
%信号发射
baseband_out = round(rand(1,baseband_out_length));
convert_matrix = reshape(baseband_out,bits_per_symbol,length(baseband_out)/bits_per_symbol);
for k = 1:(length(baseband_out)/bits_per_symbol)
    modulo_baseband(k) = 0;
    for i = 1:bits_per_symbol
        modulo_baseband(k) = modulo_baseband(k) + convert_matrix(i,k)*2^(bits_per_symbol - i);
    end
end

carrier_matrix = reshape(modulo_baseband, carrier_count,symbols_per_carrier);
%QDPSK调制
carrier_matrix = zeros(1,carrier_count);
for i = 2:(symbols_per_carrier +1)
    carrier_matrix(i,:) = rem(carrier_matrix(i,:) + carrier_matrix(i-1,: ), 2^bits_per_symbol);
end

carrier_matrix = carrier_matrix * ((2* pi) / (2^bits_per_symbol));
[X,Y] = pol2cart(carrier_matrix, ones(size (carrier_matrix, 1), size(carrier_matrix, 2)));
complex_carrier_matrix = complex(X,Y);
%加训练序列
training_symbols = [1 j j 1 -1 - j- j-1 1 j j1 -1 - j-j-1 1 j j 1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1 -1- j- j-1 1 j j 1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1-1 - j- j-1 1 j j 1 -1 - j- j-1 1 j j 1 -1 - j- j-1 1 j j 1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1 -1 - j
- j-1 1 j j1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j 1 -1 - j- j-1 1 j j 1 -1 - j- j-1 1j j 1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j 1 -1 - j- j-1 1 j j1 -1 - j- j-1 1 j j1 -1 -j- j-1 1 j j 1 -1 - j- j-1];
training_symbols = cat(1, training_symbols, training_symbols);

complex_carrier_matrix = cat(1, training_symbols, complex_carrier_matrix);
IFFT_modulation = zeros(4 + symbols_per_carrier + 1,IFFT_bin_length);
IFFT_modulation(:,carriers) = complex_carrier_matrix;IFFT_modulation(:, conjugate_carriers)= conj (complex_carrier_matrix);
time_wave_matrix = ifft(IFFT_modulation');
time_wave_matrix = time_wave_matrix';
for i = 1:4 + symbols_per_carrier+1
    windowed_time_wave_matrix(i,:) = real(time_wave_matrix (i,:));
end
ofdm_modulation = reshape(windowed_time_wave_matrix',1, IFFT_bin_length*(4 +symbols_per_carrier+1));
Tx_data = ofdm_modulation;
%信道
d1 = 4; a1 = 0.2; d2 = 5; a2 = 0.3; d3 = 6; a3 = 0.4;
d4 = 7; a4 = 0.5;
copy1 = zeros(size(Tx_data));
for i= 1 +d1: length(Tx_data)
    copy1(i) = a1*Tx_data( i-d1);
end
copy2 = zeros(size(Tx_data));
for i= 1 +d2: length(Tx_data)
    copy1(i) = a2*Tx_data(i-d2);
end
copy3 = zeros(size(Tx_data));
for i= 1 +d3: length(Tx_data)
    copy1(i) = a3*Tx_data(i-d3);
end
copy4 = zeros(size(Tx_data));
for i= 1 +d4: length(Tx_data)
    copy1(i) = a4*Tx_data(i-d4);
end
Tx_data = Tx_data+copy1 +copy2;

Tx_signal_power = var(Tx_data);
linear_SNR = 10^(SNR/10);
noise_sigma = Tx_signal_power/linear_SNR;
noise_scale_factor = sqrt(noise_sigma);
noise = randn(1, length(Tx_data))* noise_scale_factor;
Rx_Data = Tx_data + noise;

%信号接收
Rx_Data_matrix = reshape(Rx_Data, IFFT_bin_length, 4+symbols_per_carrier+1);
Rx_spectrum = fft(Rx_Data_matrix);
Rx_carriers = Rx_spectrum(carriers,: ) ;
Rx_training_symbols = Rx_carriers((1: 4),: );
Rx_carriers = Rx_carriers((5: 55),: );

%信道估计
Rx_training_symbols = Rx_training_symbols./training_symbols;
Rx_training_symbols_deno = Rx_training_symbols.^2;
Rx_training_symbols_deno =Rx_training_symbols_deno(1,: ) +Rx_training_symbols_deno(2,: ) +Rx_training_symbols_deno(3,: ) +Rx_training_symbols_deno(4,: );
Rx_training_symbols_nume = Rx_training_symbols(1,: ) +Rx_training_symbols(2,: ) + Rx_training_symbols(3,: ) +Rx_training_symbols(4,: );
Rx_training_symbols_nume =conj(Rx_training_symbols_nume);
Rx_training_symbols = Rx_training_symbols_nume./Rx_training_symbols_deno;
Rx_training_symbols_2 = cat(1,Rx_training_symbols,Rx_training_symbols);
Rx_training_symbols_4 = cat(1,Rx_training_symbols_2,Rx_training_symbols_2);
Rx_training_symbols_8 = cat(1,Rx_training_symbols_4,Rx_training_symbols_4);
Rx_training_symbols_16= cat(1,Rx_training_symbols_8,Rx_training_symbols_8);
Rx_training_symbols_32= cat(1,Rx_training_symbols_16,Rx_training_symbols_16);
Rx_training_symbols_48= cat(1,Rx_training_symbols_32,Rx_training_symbols_16);
Rx_training_symbols_50= cat(1,Rx_training_symbols_48,Rx_training_symbols_2);
Rx_training_symbols = cat(1,Rx_training_symbols_50,Rx_training_symbols);
Rx_carriers = Rx_training_symbols.*Rx_carriers;
Rx_phase = angle(Rx_carriers)* (180/pi);
phase_negative = find(Rx_phase < 0);
Rx_phase(phase_negative) = rem(Rx_phase(phase_negative) +360, 360);
Rx_decoded_phase = diff(Rx_phase);
phase_negative = find(Rx_decoded_phase < 0);
Rx_decoded_phase(phase_negative) = rem(Rx_decoded_phase(phase_negative) +360, 360);
% QDPSK解调
base_phase = 360/2^bits_per_symbo;l
delta_phase = base_phase/2;
Rx_decoded_symbols = zeros(size(Rx_decoded_phase, 1),size(Rx_decoded_phase, 2));

%
for i = 1: (2^bits_per_symbol-1)
    center_phase = base_phase*i;
    plus_delta = center_phase+delta_phase;
    minus_delta = center_phase-delta_phase;
    decoded = find((Rx_decoded_phase <= plus_delta) & (Rx_decoded_phase > minus_delta));
    Rx_decoded_symbols(decoded) =i;
end
Rx_serial_symbols = reshape(Rx_decoded_symbols ,1,size(Rx_decoded_symbols, 1)* size(Rx_decoded_symbols,2));
for i = bits_per_symbo:l -1: 1
    if i ~= 1
        Rx_binary_matrix(i,: ) = rem(Rx_serial_symbols, 2);
        Rx_serial_symbols = floor(Rx_serial_symbols/2);
    else
        Rx_binary_matrix(i,: ) = Rx_serial_symbols;
    end
end
baseband_in = reshape(Rx_binary_matrix, 1,size(Rx_binary_matrix, 1)* size(Rx_binary_matrix, 2));

%误码率计算
bit_errors = find(baseband_in ~= baseband_out);
bit_error_count = size(bit_errors, 2);
total_bits = size(baseband_out, 2);
bit_error_rate = bit_error_count/total_bits;
fprintf(f\n, bit_error_rate);