Write the MATLAB codes to realize the modulation and demodulation simulations of QPSK in AWGN. Draw the BER versus SNR curve and compare the results with theoretical results.

提问人：网友zjc_anchor 发布时间：2022-01-07

参考答案

抱歉！暂无答案，正在努力更新中……

如搜索结果不匹配，请联系老师获取答案

您可能会需要：

重置密码查看订单联系客服

安装简答题APP，拍照搜题省时又省心！

更多“Write the MATLAB codes to real…”相关的问题

第1题

Course: Digital Image Processing Homework: #1 Due ...

Course: Digital Image Processing Homework: #1 Due date: 15/3/2020 Note: 1) This is individual work. However, peer discussion is encouraged. 2) For every programming assignment, clearly document your code so that the Teaching Associate (TA) can follow your algorithms easily. Include the output images of your functions in the report. *************************************************************************** Question 1. Using the attached two images lenna_low_contrast with size 256 by 256, and lenna_original with size 512 by 512, write your Matlab codes to perform the following tasks: a. Up-sampling images lenna_low_contrast to size 512 by 512, and show the processed image IUS; b. Down-sampling the image lenna_original to size 256 by 256, and show the processed image IDS; c. Give your comments on processed images IUS and IDS. Please tell the difference of up-sampling and down-sampling processing. Question 2. The following table gives the number of pixels at each of the gray levels 0-15 in an image with those gray values only. a. Draw the histogram corresponding to these gray levels b. Perform histogram equalization c. Draw the resulting histogram. d. Analyze why the resulting histogram is not exactly same 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 40 60 75 80 75 65 55 50 45 40 35 30 25 20 30 Question 3. Write your Matlab code to enhance the image lenna_low_contrast by using a. appropriate point processing; b. histogram equalization; c. spatial filter d. Compare and analyze their performances.

点击查看答案

第2题

Write MATLAB codes to implement DSSS system sinmul...

Write MATLAB codes to implement DSSS system sinmulation, The system is a 2-user system with a 2-path frequency selective fadingchannel for each user, The power for each path is half the total transmit power for each user. A RAKR receiver with ideal channel estimation should be used in the receiver for each user. The relative delay between two paths is 1 chip. BPSK modulation is assumed. The BER-versus SNR curve should be drawn for both users. The SNR is defined by, and is assumed to change from 0 dB to 10 dB with step 1 dB. The simulation results should be compared with the theoretical BER versus SNR curve in flat Rayleigh fading channel.

A、N_Trials=300; N_number=100; N_snr=10; N_users=2; N_path=2; Delay=1; Q=16; N_samples=N_number*Q; E_M=0; for trials=1:N_Trials trials noise=randn(N_users.*N_path,Q*N_number)+j.*randn(N_users*N_path,Q*N_number); s10=round(rand(N_users,N_number)); ss_user=s10*2-1; % without consideration of delayed path pn01=round(rand(N_users,Q)); pn=(pn01.*2-1)./sqrt(Q); S_spread=[]; for user=1:N_users s=kron(ss_user(user,:), pn(user,:)); S_spread=[S_spread; s]; end S_multipath=[]; for user=1:N_users s_user=S_spread(user,:); Suser_delayed=[zeros(1,Delay), s_user(1,1:(N_samples-Delay))]; S_multipath=[S_multipath; [s_user;Suser_delayed]]; end %___________the following is to generate Rayleigh fading coefficients N=N_users.*N_path; fad_c=fading(8,0.005,N_samples, N); fad=fad_c.'; % only use the amplitude; %phase is not used, which is equivalent to have an ideal phase %compensation by using channel equalization S_spread=S_multipath.*fad; %S_spread is the signal matrix of size users x N_number sgma=1; Error_M=[ ]; for snr_db=0:1:N_snr snr=10.^(snr_db./10)/2; %Evaluate the SNR from SNR in dB N0=2*sgma.^2; Eb=snr.*N0; yy=sqrt(Eb./2)*S_spread+noise; %received spread signals in the baseband Error_v_user=[]; for user=1:N_users y_path1=yy((user-1).*N_path+1, :); y_path_temp=yy((user-1).*N_path+2, :); y_path2=[y_path_temp(1,Delay+1:N_samples), zeros(1,Delay)]; Y_M_path1=[ ]; Y_M_path2=[]; for k=1:N_number ym1=y_path1(1,(k-1)*Q+1:k*Q); ym2=y_path2(1,(k-1)*Q+1:k*Q); Y_M_path1=[Y_M_path1;ym1]; Y_M_path2=[Y_M_path2;ym2]; end % Y_M is a matrix of size N_number x Q, each row correspinding to a % BPSK symbol ys=Y_M_path1*pn(user,:).'+Y_M_path2*pn(user,:).'; %despreading for a user y=ys.'; y_real=real(y); s_e=sign(y_real); s_e10=(s_e+1)./2; Error_snr=sum(abs(s10(user,:)-s_e10)); Error_v_user=[Error_v_user;Error_snr./N_number]; %A BER collumn vector for all the users and for each snr end %for user Error_M=[Error_M,Error_v_user]; %BER matrix for all users and for all the SNRs end % end for snr E_M=E_M+Error_M ; end % end for trials BER=E_M./N_Trials; BER_T=[ ]; for snr_db=0:1:N_snr snr=10.^(snr_db./10); temp=sqrt(snr./(1+snr)); BER_THEROY=(1-temp)/2; BER_T=[BER_T,BER_THEROY]; end i=0:1:N_snr; semilogy(i,BER(1,:),'-r',i,BER(2,:),'ob',i,BER_T ,'*g'); xlabel('E_b/N_0(dB)') ylabel('BER') legend('User1 RAKE','User2 RAKE', 'Theoretical flat fading');