Matlab__Simulink for Digital Communication课件.ppt

CHAPTER 4 AD (Analog-to-Digital) Conversion;4.1.1 Uniform Quantization;%quantize_uniform.m: Uniform Quantization (Fig.4.1) % gives boundary vector b, quantization level vector c, and MSQE clear, clf pdf=exp(-(x-m).^2/2/sigma^2)/sqrt(2*pi)/sigma; % Gaussian pdf of x xf=inline([x.* pdf],x,m,sigma); f=inline(pdf,x,m,sigma); m=0; sigma=1; % Mean and variance of the random variable x b0=-3; bN=3; % Given least/greatest value of the random variable x for N=5:6 % Number of quantization intervals delta=(bN-b0)/N; b=b0+[0:N]*delta; msqe=0; % Mean-Square Quantization Error for i=1:N % Centroid of each interval tmp1=quad(xf,b(i),b(i+1),0.01,[],m,sigma); tmp2=quad(f,b(i),b(i+1),0.01,[],m,sigma); tmp=tmp1/tmp2; c(i)=tmp; % Eq.(4.1.1) x2f=inline([(x-tmp).^2.* pdf],x,m,sigma,tmp); msqe=msqe+quad(x2f,b(i),b(i+1),0.01,[],m,sigma,tmp); % Eq.(4.1.2) end b,c % Resulting boundary vector and centroid vector x=b0+(bN-b0)/1000*[0:1000]; y(find(xb(1)))=c(1); % Left-most interval for i=1:N, y(find(b(i)=xxb(i+1)))=c(i); end y(find(x=b(N+1)))=c(N); % Right-most interval subplot(2,2,N-4), plot(x,y), hold on, grid on % Quantization graph m=0; sigma=1; fx=feval(f,x,m,sigma); plot(x,fx,r:); msqe % Resulting MSQE end; ;;4.1.3 Non-uniform Quantization Considering Relative Errors;function [d,dcode]=ADC(a,b,c,code_table) % Analog-to-Digital Conversion % Input : a = Analog signal % b = Boundary vector % c = Centroid vector % code_table = Code table % Output: d = Quantized samples % dcode = The corresponding code N=length(c); if nargin4, code_table=[0:N-1]; end Na=length(a); % dcode=zeros(Na,size(code_table,2)); for n=1:Na I=find(a(n)b(2:N)); if ~isempty(I), d(n)=c(I(1)); dcode(n,:)=code_table(I(1),:); else d(n)=c(N); dcode(n,:)=code_table(N,:); end end;;%sim_PCM.m % simulates the PAM/PCM(Pulse Amplitude/Code Modulation) system clear, clf T=0.01; nmax=200; t=[0:nmax-1]*T; b0=-1; bN=1; N=