ofdm符号定时与频偏联合估计算法matlab实现

%%%%最大似然ML 最大似然方法联合实现符号定时同步和载波同步仿真 clear all; %********************** preparation part *************************** para=128; % Number of parallel channel to transmit (points) %并行信道传输个数 fftlen=128; % FFT length %fft 长度 noc=1024; % Number of carrier %载波的个数 nd=6; % Number of information OFDM symbol for one loop%OFDM 符号个数 ml=2; % Modulation level : QPSK %调制方式：QPSK sr=250000; % Symbol rate %符号的比特率 br=sr.*ml; % Bit rate per carrier %每个载波的比特率? gilen=128; % Length of guard interval (points)%%保护间隔的长度 ebn0=3; % Eb/N0 %Eb为单位比特的平均信号的能量；n0为噪声的单边功率普密度 SNR=15; % ENR %信噪比 T=10^(-6); % Sampling time : 1us%%采样时间1us 抽样频率1024kHz %************************** transmitter发射机 ********************************* %************************** Data generation 数据生成程序**************************** seldata=rand(1,para*nd*ml)>0.5; % rand : built in function %产生1x(128*6*2)=1X1536的数据 M=length(seldata)/noc; %****************** Serial to parallel conversion串并转换 *********************** paradata=reshape(seldata,para,nd*ml); % reshape : built in function %************************** QPSK modulation %QPSK 调制***************************** [ich,qch]=qpskmod(paradata,para,nd,ml); kmod=1/sqrt(2); % sqrt : built in function ich1=ich.*kmod;%实部的数据 qch1=qch.*kmod;%虚部的数据 %******************* IFFT ************************ x=ich1+qch1.*i; y=ifft(x); % ifft : built in function ich2=real(y); % real : built in function qch2=imag(y); % imag : built in function %********* cyclic perfix insertion 循环保护间隔********** [ich3,qch3]= giins(ich2,qch2,fftlen,gilen,nd); fftlen2=fftlen+gilen; %***************** AWGN addition 叠加在 AWGN信道上********* ich4=AWGN(ich3,SNR); qch4=AWGN(qch3,SNR); rx=ich4+qch4.*i;%叠加噪声之后的数据 %****************** Calculate gamma(m) & PI(m) ******************* gamma =zeros(M,noc); lamda =zeros(M,noc); pii =zeros(M,noc); ro = SNR/(SNR+1); e=0.25; for n=1:M for theta=1:noc sampling_rx(n,:)=[rx(n,1:theta) rx(n,:) rx(n,1:noc-theta)]; %sampling_rx=[rx(n,1:theta) rx(n,:) rx(n,1:noc-theta)]; %sampling_rx(n,:)=[zeros(1,theta) rx(n,:) zeros(1,noc-theta)]; for k=theta:theta+gilen-1 %equation 2-(6),2-(7) gamma(n,theta)= gamma(n,theta)+sampling_rx(n,k)*conj(sampling_rx(n,k+noc)); pii(n,theta)=pii(n,theta)+abs(sampling_rx(n,k))^2 + abs(sampling_rx(k+noc))^2; end gamma(n,theta) pii(n,theta)=0.5*pii(n,theta); %equation 2-(5) lamda(n,theta)=abs(gamma(n,theta))*cos(2*pi*e+angle(gamma(n,theta))) - ro*pii(n,theta); end end t=T:T:noc*T; figure(8); subplot(4,1,1); plot(abs(gamma(1,:))) subplot(4,1,2); plot(pii(1,:)) subplot(4,1,3); plot(lamda(1,:)) subplot(4,1,4); plot(real(sampling_rx(1,:))) figure(9); plot(t,lamda(1,:))