【正文】 nd 　% points (at the discontinuites)% this reduces the effects of the discontinuities　　% it also distorts the desired frequency response (undesired side effect)　% between Blackman, Hanning, and Hamming: Hamming introduces less distortion% note that the transpose of the Hamming function is % used (because a row vector is needed)%　% Since all imaginary values of time_wave_matrix are practically equal to zero,% only the real part is retained for windowing.　　%　　for i = 1:symbols_per_carrier + 1　 %windowed_time_wave_matrix(i,:) = real(time_wave_matrix(i,:)) .* hamming(IFFT_bin_length)39?！? windowed_time_wave_matrix(i,:) = real(time_wave_matrix(i,:))。end　%　% Serialize the modulating waveform　　% sequentially take each row of windowed_time_wave_matrix and construct a row vector　% the row vector will be the modulating signal　　% note that windowed_time_wave_matrix is transposed, this is to account for the way the　% Matlab 39。reshape39。 function works (reshape takes the columns of the target matrix and 　% appends them sequentially)% ofdm_modulation = reshape(windowed_time_wave_matrix39。, 1, IFFT_bin_length*(symbols_per_carrier+1))。　%　　% PLOT OFDM SIGNAL (time)　%　　%temp_time = IFFT_bin_length*(symbols_per_carrier+1)。%figure (5)　%plot(0:temp_time1,ofdm_modulation)%grid on　%ylabel(39。Amplitude (volts)39。)%xlabel(39。Time (samples)39。)　%title(39。OFDM Time Signal39。)%　　% PLOT OFDM SIGNAL (spectrum)%symbols_per_average = ceil(symbols_per_carrier/5)?！　?avg_temp_time = IFFT_bin_length*symbols_per_average?！　?averages = floor(temp_time/avg_temp_time)?！?average_fft(1:avg_temp_time) = 0?！　?for a = 0:(averages1)% subset_ofdm = ofdm_modulation(((a*avg_temp_time)+1):((a+1)*avg_temp_time))。% subset_ofdm_f = abs(fft(subset_ofdm))?！? average_fft = average_fft + (subset_ofdm_f/averages)?！?end　　%average_fft_log = 20*log10(average_fft)?！?figure (6)　%plot((0:(avg_temp_time1))/avg_temp_time, average_fft_log)　%hold on　%plot(0:1/IFFT_bin_length:1, 35, 39。rd39。)%grid on%axis([0 40 max(average_fft_log)])　%ylabel(39。Magnitude (dB)39。)　　%xlabel(39。Normalized Frequency ( = fs/2)39。)　　%title(39。OFDM Signal Spectrum39。)　%　% ENDPLOT　%% Upconversion to RF 　　%　　% For this model, the baseband will be inserted directly into the channel　　% without conversion to RF frequencies.　%Tx_data = ofdm_modulation?！?　%CHANNEL ======================================================================　%　　% The channel model is Gaussian (AWGN) only 　　% Rayleigh fading would be a useful addition　　%Tx_signal_power = var(Tx_data)。　　%linear_SNR = 10^(SNR/10)。　noise_sigma = Tx_signal_power/linear_SNR?！　oise_scale_factor = sqrt(noise_sigma)?！　?　　noise = randn(1, length(Tx_data))*noise_scale_factor?！　x_Data = Tx_data + noise。%　　%%RECEIVE 　　%% Convert the serial input data stream to parallel (according to symbol length 　% and number of symbols) % each column is a symbol period　　% the length of each symbol (samples per symbol) is the length of the 　% IFFT that was used to generate it%　Rx_Data_matrix = reshape(Rx_Data, IFFT_bin_length, symbols_per_carrier + 1)。%% Transform each symbol from time to frequency domain% take the fft of each column%　　Rx_spectrum = fft(Rx_Data_matrix)。%% PLOT BASIC FREQUENCY DOMAIN REPRESENTATION%%figure (7)　%stem(0:IFFT_bin_length1, abs(Rx_spectrum(1:IFFT_bin_length,2)),39。b*39。)　　%grid on　%axis ([0 IFFT_bin_length ])%ylabel(39。Magnitude39。)%xlabel(39。FFT Bin39。)　%title(39。OFDM Receive Spectrum, Magnitude39。)　%figure (8)%plot(0:IFFT_bin_length1, (180/pi)*angle(Rx_spectrum(1:IFFT_bin_length,2)), 39。go39。)　　%hold on　　%stem(carriers1, (180/pi)*angle(Rx_spectrum(carriers,2)),39。b*39。)%stem(conjugate_carriers1, (180/pi)*angle(Rx_spectrum(conjugate_carriers,2)),39。b*39。)　　%axis ([0 IFFT_bin_length 200 +200])　%grid on　%ylabel(39。Phase (degrees)39。)　 　%xlabel(39。FFT Bin39。)%title(39。OFDM Receive Spectrum, Phase39。)　%% END OF PLOTTING　　%% Extract the carrier FFT bins　　% only keep the fft bins that are used as carriers　　% take the transpose of the result so that each column will represent 　% a carrier　　% this is in preparation for using the diff( ) function later to decode 　% differential encoding% format following this operation is:%　　% C1s1 C2s1 C3s1 ...% C1s2 C2s2 C3s2 ...　% C1s3 C2s3 C3s3 ...% . . .% . . .　% 　% IMPORTANT MATLAB NOTE CONCERNING TRANSPOSING AND CONJUGATION% it appears that each time a matrix is transposed, the conjugate of 　% each value is taken　　% if an even number of transposes are done, then it is transparent　　% obviously, this does not affect real numbers　%　　Rx_carriers = Rx_spectrum(carriers,:)39。%% PLOT EACH RECEIVED SYMBOL　　%figure (9)　　Rx_phase_P = angle(Rx_carriers)。　　Rx_mag_P = abs(Rx_carriers)。　polar(Rx_phase_P, Rx_mag_P,39。bd39。)。　%　　% END PLOT　　%　% Find the phase (angle) of each FFT bin (each carrier)% convert from radians to degrees　% normalize phase to be between 0 and 359 degrees%　Rx_phase = angle(Rx_carriers)*(180/pi)。　　phase_negative = find(Rx_phase 0)。Rx_phase(phase_negative) = rem(Rx_phase(phase_negative)+360,360)?！　?　% Extract phase differences (from the differential encoding)　　% the matlab diff( ) function is perfect for this operation　% again, normalize the result to be between 0 and 359 degrees　　%　Rx_decoded_phase = diff(Rx_phase)?！hase_negative = find(Rx_decoded_phase 0)?！x_decoded_phase(phase_negative) = rem(Rx_decoded_phase(phase_negative)+360,360)?！?　% Convert phase to symbol　　%