【信道估计】特征分解GFDM无ICI、ISI干扰和导频插入信道估计【含Matlab源码 2671期】.zip

%******Pedestrian-A 信道BER仿真********% clc clear all %% 参数设置 N=128; %子载波数目 M=5; %每个子载波里的字符号数目 qam_M=64; %QAM调制阶数 psk_M=4; %PSK调制阶数 mod_M=[qam_M psk_M]; g_ori=zeros(2,M*N+1); g_ori_final=zeros(2,M*N); B_all=zeros(2*M*N,2*M*N); B=mat2cell(B_all,[M*N,M*N],2*M*N); %% 建立PA信道 ts=1/640; fd=8.3*2/3.8; tau=[0 200 800 1200 2300 3700]*10^(-9); pdb=[0 -0.9 -4.9 -8.0 -7.8 -23.9]; chan=comm.RayleighChannel('SampleRate',1/ts, ... 'PathDelays',tau, ... 'AveragePathGains',pdb, ... 'MaximumDopplerShift',fd); %% %% 建立滤波器 beta=[0.5 0.9]; % 滚降系数 for ig=1:2 g_ori(ig,:)=rcosdesign(beta(ig),M,N); % g_ori为升余弦滚降滤波器（1×641） g_ori_final(ig,:)=g_ori(ig,2:641); g(:,ig)=g_ori_final(ig,:)'; % 将g_ori转置 G=zeros(M*N,2*M); % 构造矩阵G for i=1:10 % 对g进行循环移位 G(:,i)=circshift(g(:,ig),64*(i-1)); end [V,L,U]=svd(G); % L对角元素是对G'G进行特征分解得到特征值矩阵对角元素的平方根，特征值降序排列 L_diag=diag(L); L_unnegative=L_diag(1:5,:); % 取L的前五个非0值 P = zeros(10,10); %建立功率分配矩阵P P(1:5,1:5)=diag(1./L_unnegative); P(6:10,6:10)=0; % P(2M×2M)(10×10) Qpre=U; %因为G特征分解得到的矩阵U应与G'G特征分解得到的特征向量矩阵相等，故直接取Qpre=U Qpost=U'; I=eye(128,128); P_bl=kron(I,P); U_bl=kron(I,U); A=zeros(M*N,2*M*N); w=zeros(N,1); for iw=1:128 for jw=0:127 w(jw+1)=exp(1j*2*pi*jw*iw/N); end A(:,10*(iw-1)+1:10*iw)=diag([w' w' w' w' w']')*G; end B(ig)={A*U_bl*P_bl}; end B1=B(1); %滚降系数为0.5的GFDM调制矩阵 B2=B(2); %滚降系数为0.9的GFDM调制矩阵 %% times=10; ebno=0:10; num_err_all=zeros(2*times,2*length(ebno)); num_err=mat2cell(num_err_all,[times times],[length(ebno) length(ebno)]); E2R=[10*log10((6)) 10*log10((2))]; %Eb/N0与SNR之间的转换因子 for ix=1:times %*****产生信号源******% x_ori_qam=randi([0, 1], 5*log2(qam_M), 128); % 随机生成一个0、1组成的30行128列数据矩阵x_ori(M*Mlog2(QAM)×N)(30×128) x_con_qam=qammod(x_ori_qam, qam_M, 'bin', 'InputType', 'bit', 'OutputDataType', numerictype(1,16,10)); % 对x_ori进行64QAM调制得到信号x_con(M×N)(5×128) x_ori_psk=randi([0, 1], 5*log2(psk_M), 128); % 随机生成一个0、1组成的10行128列数据矩阵x_ori(M*Mlog2(QAM)×N)(10×128) x_con_psk=qammod(x_ori_psk, psk_M, 'bin', 'InputType', 'bit', 'OutputDataType', numerictype(1,16,10)); % 对x_ori进行64QAM调制得到信号x_con(M×N)(5×128) x_ori_all=[x_ori_qam;x_ori_psk]; x_ori=mat2cell(x_ori_all,[30, 10]); %*****对x进行补零***** o=zeros(M,N); D_qam=[x_con_qam;o]; % x(2M×N)(10×128) d_qam=reshape(D_qam,2*M*N,1); % d(1280×1) D_psk=[x_con_psk;o]; % x(2M×N)(10×128) d_psk=reshape(D_psk,2*M*N,1); % d(1280×1) s_all=zeros(1280,2); s=mat2cell(s_all,[640 640],[1 1]); s(1,1)={cell2mat(B1)*d_qam}; %QAM with 滚降系数0.5 s(1,2)={cell2mat(B2)*d_qam}; %QAM with 滚降系数0.9 s(2,1)={cell2mat(B1)*d_psk}; %PSK with 滚降系数0.5 s(2,2)={cell2mat(B2)*d_psk}; %PSK with 滚降系数0.9 % 通过PA信道 并加噪声 F=zeros(1,length(ebno)); K=zeros(1,length(ebno)); for isr=1:2 for isc=1:2 ss=cell2mat(s(isr,isc)); for i=1:length(ebno) snr= E2R(isr)+(i-1); sigPower=10*log10(sum(abs(ss.data).^2)/length(ss.data)); s_pa=chan(double(ss)); %通过PA信道 H=fft(s_pa)./fft(ss.data); s_add_noise=awgn(s_pa,snr,sigPower); %加高斯白噪声 eq_signal=fft(s_add_noise)./H; s_recovered=cell2mat(B(isc))'*ifft(eq_signal); d_recovered=reshape(s_recovered,2*M,N); x_recovered=d_recovered(1:M,:); %去零 x_demod=qamdemod(x_recovered,mod_M(isr), 'bin', 'OutputType', 'bit'); [F(i),K(i)]=symerr(cell2mat(x_ori(isr)),x_demod); end num_err{isr,isc}(ix,:)=F; end end end figure(1) ber_all=zeros(2,2*length(ebno)); ber=mat2cell(ber_all,[1 1],[length(ebno) length(ebno)]); ber(1,1)={sum(cell2mat(num_err(1,1)))/(times*5*log2(qam_M)*N)}; ber(1,2)={sum(cell2mat(num_err(1,2)))/(times*5*log2(qam_M)*N)}; ber(2,1)={sum(cell2mat(num_err(2,1)))/(times*5*log2(psk_M)*N)}; ber(2,2)={sum(cell2mat(num_err(2,2)))/(times*5*log2(psk_M)*N)}; semilogy(ebno,cell2mat(ber(1,1)),'-r'); hold on semilogy(ebno,cell2mat(ber(1,2)),'or'); hold on semilogy(ebno,cell2mat(ber(2,1)),'--b'); hold on semilogy(ebno,cell2mat(ber(2,2)),'ob'); legend('β=0.5,64QAM','β=0.9,64QAM','β=0.5,QPSK','β=0.9,QPSK'); xlabel('Eb/No [dB]'); ylabel('BER'); title('Pedestrian-A channel'); grid(); axis([0 10 10^(-6) 1])