SIMULATION

OF OFDM

Simulation using

MATLAB under practical

conditions

Kishan P B

MATLAB CODE: % OFDM.m % Written by: KISHAN P B % 4th year B.E student, % Dept. of ECE, PESIT % No.of Carriers: 64 % coding used: Convolutional coding + trellis % Single frame size: 96 bits % Total no. of Frames: 100 % Modulation: 16-QAM % No. of Pilots: 4 % Cylic Extension: 25%(16 bits) % Final Packet size: 80 %% % Your wish to use the following % close all % clear all % clc tt = tic(); %% % Initialisations Single_frame_size = 96; Total_no_of_Frames = 100; scatter_plot_frame = 48; x=1; %Frame distinction xx=1; %Scatter plot distinction si=1; %for BER rows pilt=3+3j; % Pilot complex insertion data no_of_Carriers = 64; % Data to be transmitted without CP/CE initial_test_snr = 0; snr_step_size = 2; final_test_snr = 50; BER = zeros( Total_no_of_Frames ,floor( (final_test_snr+snr_step_size)/snr_step_size )); % To store final Bit error rate results; here it is 100*26 no_of_pilots = 4; no_of_data_without_carriers = (Single_frame_size*2/no_of_pilots) + no_of_pilots; % 52 in this case ; derived from line "x" cyclic_extension = 16; choice_snr = 14; % choose a valid snr value to plot the result ---- see below %% % Generating data t_data = floor(2*rand(1,Single_frame_size*Total_no_of_Frames)); t_rxed_data=zeros(2,Single_frame_size*Total_no_of_Frames);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%HERE THE CORE CODE EXISTS%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% IF YOU ARE IMPRESSED BY THE RESULTS AND %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% NEED THE CODE, THEN MAIL ME!!! %% % Time averaging for optimum results ber = zeros(1,26); BER = BER'; for col=1:26; %%%change if SNR loop Changed ber(1,col)=ber(1,col)+sum(BER(col,:)); end ber=ber./(Single_frame_size*Total_no_of_Frames*log2(M)); % Normalisation %%% RESULTS %% % Scatter Plot % Create scatter plot of noisy signal and transmitted % signal on the same axes. h = scatterplot(scatter_plot_requirement(2,:),1,0,'g.'); hold on; scatterplot(scatter_plot_requirement(1,:),1,0,'k*',h); choice_snr = num2str(choice_snr); title_print = strcat('received Signal of SNR = ',choice_snr); title(title_print); legend('Noisy received Signal','Signal Constellation'); axis([-5 5 -5 5]); % Set axis ranges. hold off; %% % Plot for comparison between original signal wrt processed rxed signal figure; space = 10; t = 1:space:Single_frame_size*Total_no_of_Frames; subplot(211); y = [t_rxed_data(1,1:space:Single_frame_size*Total_no_of_Frames);t_data(1:space:Single_frame_size*Total_no_of_Frames)]'; h = stem(t,y); set(h(1),'MarkerFaceColor','blue') set(h(2),'MarkerFaceColor','red','Marker','square') title('Blue --> Processed received Signal at SNR = 50 & Red --> Original generated data'); subplot(212); y = [t_rxed_data(2,1:space:Single_frame_size*Total_no_of_Frames);t_data(1:space:Single_frame_size*Total_no_of_Frames)]'; h = stem(t,y); set(h(1),'MarkerFaceColor','blue') set(h(2),'MarkerFaceColor','red','Marker','square')

title_print = strcat('Blue --> Processed received Signal of SNR = ',choice_snr,' & Red --> Original generated data'); title(title_print); %% % BER vs SNR graph figure; i=0:2:16; theoryBer = (1/4)*3/2*erfc(sqrt(4*0.1*(10.^(i/10)))); semilogy(i,theoryBer,'bs-','LineWidth',2); hold on semilogy(i,ber(1:9),'mx-','LineWidth',2); title('BER vs SNR'); ylabel('Normalised BER'); xlabel('SNR (dB)'); grid on legend('theory', 'simulation'); %% toc(tt); % Processing time on my laptop was ~2 seconds

RESULTS IN GRAPHS:

FIGURE 1: CONSTELLATION DIAGRAM

FIGURE 2: ORIGINAL DATA AND RECEIVED DATA

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In-Phase

received Signal of SNR =14

Noisy received SignalSignal Constellation

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1Blue --> Processed received Signal at SNR = 50 & Red --> Original generated data

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1Blue --> Processed received Signal of SNR =14 & Red --> Original generated data

FIGURE 3: THEORETICAL AND SIMULATED BER CURVES

REFERENCES:

1. Kishan P B, Orthogonal Frequency Division Multiplexing, www.academia.edu 2014. 2. MatLab help

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100BER vs SNR

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SNR (dB)

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