Matlab sort out

Personal matlab sort out about communication simulations finally

1.Basics

• Scalars

a = 10
a = 10+1i       % 推荐使用1i

• Vectors

a = [1 3 5]     % 1行3列
a = [1,3,5]     % 使用,与否效果相同
a = 1:2:7       % begin:step:end
a = [1;3;5]     % 3行1列

• Matrices

A = [1,2,3;4,5,6;7,8,9] % 三行三列矩阵

• Operations

1. * 和.*的区别

   在进行数值运算和数值乘矩阵运算，两者无区别，a*b=a.*b; a*B=a.*B; B*a=B.*a（其中小写字母表示数值，大写字母表示矩阵，下同）。
   在处理矩阵乘矩阵时，*表示普通的矩阵乘法，要求前面矩阵的列数等于后面矩阵的行数；.*表示两个矩阵对应元素相乘，要求两个矩阵行数列数都相等。

   >> [1,2,3]*[1,2;3,4;5,6]       % 矩阵乘法
   ans =
   22    28
   
   >> [1,2,3].*[4,5,6]            % 矩阵点乘
   ans =
   4    10    18

2. / 和./的区别

   数值运行时，这两种没有区别，a/b=a./b
   数值与矩阵运算，数值在前时只能用./;数值在后时a/b=a./b
   矩阵与矩阵，A/B可以看作是A*inv(B);A./B表示A矩阵和B矩阵对应元素相除，需要矩阵维度保持一致。
   另外有\和.\表示前除，类似小学时候详细区的分除和被除运算，一般习惯不使用这种。

   >> [4,5]/[1,2;3,4]                    % 矩阵除法
   ans =
   -0.5000    1.5000
   
   >> [4,5,6]./[1,2,3]                   % 矩阵点除
   ans =
   4.0000    2.5000    2.0000

3. A(1,1)、A(:,1)、A(1,:)、A(:,:)

4. [1+2*1i,3+4*1i]'和[1+2*1i,3+4*1i].'都表示矩阵的转置,实数情况下A'和A.'没有区别；复数情况下A'会产生A的转置共轭矩阵，A.'产生普通的转置矩阵。

   [1+2*1i,3+4*1i]'=[1-2*1i;3-4*1i]
   [1+2*1i,3+4*1i].'=[1+2*1i;3+4*1i]

• Special number

  pi
Inf、inf:infinity,i.e.,1/0
  NaN、nan:not a number,e.g.,0/0
  eps:accuracy of the matlab,eps= 2-52 ≈ 2.2204×10-16

• General functions

  • Trigonometric functions:
    cos(x)、sin(x)、tan(x)
  • Complex:
    z = a+bi = $re^{i\theta}$
    real(z)、imag(z)、abs(z)、angle(z)、conj(z)
  • Exponential and logarithm functions

Name	Description	Name	Description
exp(x)	$e^{x}$	log1p(x)	ln(1+x)
pow2(x)	$2^{x}$	log2(x)	$log_{2}{x}$
log(x)	lnx	log10(x)	$log_{10}{x}$、$lg(x)$

• Array and matrix

Name	Description	Name	Description
ones	All one	zeros	All zeros
length	length	size	Dimension of array
sum	sum	mean	Mean of array
reshape	Reshape the array	sort	Sort the array
min	minimum	max	maximum

• Rounding functions
  round、fix、floor、ceil
• Figure plotting
  plot()、subplot(n,m,i)、figure(i)、xlabel()、ylabel()
title()、legend()、hold on、grid on、semilogy()…
• Function format
  function [outputs]=function_name(inputs)

2.Signals and linear systems

• Basic sequence

  • Impulse signal and sequence

  t = -5:0.01:5;
  y = (t==0);
  subplot(1,2,1);
  plot(t, y, 'r’);
  
  n = -5:5;
  x = (n==0);
  subplot(1,2,2);
  stem(n, x);

  • Step signal and sequence

  t =-5:0.01:5;
  y=(t>=0);
  subplot(1,2,1);
  plot(t, y, 'r')
  
  n = -5:5;
  x = (n>=0);
  subplot(1,2,2);
  stem(n, x);

  • Real exponential sequence
    x(n)=$a^n$, $\forall n$, $a\in R$

  n=[ns:nf];
  x=a.^n;

  • Exponential sequence
    x(n)=$e^{(\delta +j\omega)n}$

  n=[ns:nf];
  x=exp((delta+jw)*n);

  • Sinine, cosine sequence
    $x(n)=cos(\omega n+\theta)$

  n=[ns:nf];
  x=cos(w*n+sita);

  • Other signal generation functions

Name	Description	Name	Description
sawtooth	Sawtooth or triangle wave	pulstran	Pulse train
square	Square wave	rectpule	A period square wave
sinc	Sinc wave	tripuls	A period triangle wave

• Signal Operations

  • Moving
    y(n)=x(n-m)

  y(n)=x(n-m)

  • Periodic extension
    $y(n)=x((n))_{M}$

  y(n)=x(mod(n,M)+1)

  • Flipping
    y(n)=x(-n)

  y=fliplr(x)

  • Correlation with two sequences
    $y(m)=\sum^n x_1(n+m)x_2^*(n)$

  y=xcorr(x1,x2)

  • Cumulative sum
    $y(n)=\sum_{i=1}^n x(i)$

  y=cumsum(x)

  • Convolution of two sequences
    $y(n)=x_1(n)*x_2(n)=\sum^mx_1(m)x_x(n-m)$

  y=conv(x1,x2)

  • Convolution of two _continuous-time_ signals：
    $f(t)=\int_{-\infty}^{+\infty}f_1(\tau)f_2(t-\tau)d\tau$
    $f(t)=\sum_{k=-\infty}^{+\infty}f_1(k\Delta)f_2(t-k\Delta)\Delta$

  y=conv(x1,x2)*dt

• Fourier transform

  • Continuous-time，continuous-frequency: FT

T2F	F2T
$X(f)=\int_{-\infty}^{+\infty}x(t) e^{-j2\pi ft}dt$	$x(t)=\int_{-\infty}^{+\infty}X(f) e^{j2\pi ft}df$

• Discrete-time, discrete-frequency: DFT / FFT

T2F	F2T
$X(k)=\sum_{n=0}^{N-1}x(n) e^{-j\frac{2pi}{N}nk}$	$x(t)=\frac{1}{N}\sum_{n=0}^{N-1}X(k) e^{j\frac{2pi}{N}nk}$

• Energy and Power

  dt=t(2)-t(1);
  df=f(2)-f(1);
  N=length(t);
  T=t(end)-t(1)+dt;   % T=N*dt

  • Energy

    • T-domain
      $x[n]=x(n\Delta t)$
      $E=\int_{-\infty}^{+\infty} |x(t)|^2dt \approx\sum_{n=0}^M x[n]·x^*[n]·\Delta t$

    % E=sum(x.*conj(x))*dt;
    E=sum(abs(x).^2)*dt;

    • F-domain
      $X[k]=X(k\Delta f)$
      $E=\int_{-\infty}^{+\infty} |X(f)|^2df \approx\sum_{k=0}^{k-1} X[k]·X^*[k]·\Delta f$

    % E=sum(x.*conj(x))*df;
    E=sum(abs(x).^2)*df;

  • Power

    • T-domain
      $P=\lim_{T\rightarrow\infty}\frac{1}{T}\int_0^T |x(t)|^2dt \approx\frac{1}{N}\sum_{n=0}^{N-1} |x[n]|^2$

    % P=sum(x.*conj(x))/N;
    P=sum(abs(x).^2)/N;

    • F-domain
      _$X_Tf$ is the spectrum of x(t) within [0,T]_
      $P=\lim_{T\rightarrow\infty}\frac{1}{T}\int_{-\infty}^{+\infty} |X_T(f)|^2df \approx\frac{1}{N}\sum_{k=0}^{K-1} |X[k]|^2\Delta f$

    % P=sum(x.*conj(x))*df/T;
    P=sum(abs(x).^2)*df/T;

• Autocorrelation and Power spectral density(PSD)

  • Autocorrelation
    $x[n]=x(n\Delta t)$
    $R(\tau)=\int_{-\infty}^{+\infty} x(t)x^ (t+\tau)dt \approx\sum_{n=0}^M x[n]·x^[n+\tau]·\Delta t$
    $R_T(\tau)=\lim_{T\rightarrow\infty}\frac{R(\tau)}{T}$

    R(tau)=sum(x(t).*conj(x(t+tau))*dt/(N*dt);
    
    %% or
    R=xcorr(x);
    R=R*Ts/T;   % R=R/N

  • PSD
    $P_T(f)=\lim_{T\rightarrow\infty}\frac{|X_T(f)|^2}{T}$

  • Wiener-Khinchin theorem
    $FT(R_T(\tau))=P_T(f)$

• 🌀Component

  • hilbert change
    通过希尔伯特变换，返回的实部是本身即同向分量(quadrature component)，虚部是延迟90°后的信号即正交分量(in-phase component)

    x_a = hilbert(x);
    fc = 50;                        % Carrier fc=50Hz
    x_l = x_a.*exp(-1i*2*pi*fc*t);  % Lowpass equivalent
    x_i = real(x_l);                % In-phase component
    x_q = imag(x_l);                % Quadrature component

3.Randon process and analog modulation

For a random process $x(t)$, for an arbitrary $t_1$，$x(t_1)$ is a random variable.

• Variables and Distributions

  Function:$F_x(x)=Pr(X\leq x)$

  • Probability density function:$p(x)=\frac{dF_x(x)}{dx}$
  • Expectation:$E[X]=\int_{-\infty}^{+\infty}xp(x)dx$
  • Moment:$E[X^n] =\int_{-\infty}^{+\infty} x^n p(x)dx$
  • Variance:$E[(X-m_x)^2] =E[X^2] -m_x^2$

• Ergodicity and Stationary

  • Ergodicity

    1.The expectation of x(t) is a constant.
    mean(x)=constant
    2.Its autocorrelation only depends on the time difference.
    Ry is autocorrelation of {Yn}.
After 100 times, Ry is has nothing to do with time.

• Stationary

  1.The expectation of x(t) equals the time-average. An arbitrary realization of the random process will go through all the possible states.
  If one line has 1000 components, use mean to calculate expectation.
  Use the first component of each line, the mean of one row is time-average.

• e.g. Modulate and demodulate

• Analog modulation

  • Amplitude modulation(mainly)
    m(t)means the message signal.
    • DSB-Am

Time-domain	Frequency-domain
$s(t)=A_{c}m(t)cos(2\pi{f_c}t)$	$S(f)=\frac{A_c}{2}[M(f-{f_c})+M(f+{f_c})]$

• • • Conventional AM

Time-domain	Frequency-domain
$s(t)=A_{c}(1+am(t))cos(2\pi{f_c}t)$	$S(f)=\frac{A_c}{2}[\delta (f-{f_c})+aM(f-{f_c})+aM(f+{f_c})]$

• • • SSB-AM(T-domain)
      $s(t)=\frac{A_{c}}{2}m(t)cos(2\pi{f_c}t)\pm \frac{A_{c}}{2}m(t)sin(2\pi{f_c}t)$
      matlab code：$hilbert(m)=m(t)+j\hat{m}(t)$

U_SSB	L_SSB
$s(t)=\frac{A_{c}}{2}m(t)cos(2\pi{f_c}t)-\frac{A_{c}}{2}\hat{m}(t)sin(2\pi{f_c}t)$	$s(t)=\frac{A_{c}}{2}m(t)cos(2\pi{f_c}t)+\frac{A_{c}}{2}\hat{m}(t)sin(2\pi{f_c}t)$
$\frac{A_{c}}{2}Re[(m(t)+j\hat{m}(t))e^{j2\pi f_c t}]$	$\frac{A_{c}}{2}Re[(m(t)+j\hat{m}(t))e^{-j2\pi f_c t}]$

- e.g.:ear:

Fs = 1500;              % Sampling rate
Ts = 1/Fs;              % Sampling interval
Fc = 20;                % Carrier rate
t = 0:Ts:1;             % Time vector

m = sin(2*pi*t);        % Message signal
c = cos(2*pi*Fc*t);     % Carrier signal
Ac_DSB_AM = 1;
Ac_USSB_AM = sqrt(2);

% DBS_AM modulated signal.
u_dsb = Ac_DSB_AM*m.*c;
% SSB_AM modulated signal.
u_ussb = 1/2*Ac_USSB_AM*real(hilbert(m).*exp(1i*2*pi*Fc*t));

4.Baseband signal transmission

AWGN: add white gauss noise
Two optimum receivers for AWGN: signal correlator and matched filter.
For AWGN, the noise $N_i$ is Gaussian distributed with mean of zero and variance of $\frac{EN_0}{2}$.

• Binary modulations

  • Received signal
    • $r(t)=s_i(t)+n(t)，0\leq t \leq T_b，i=0,1$
    • Determine whether a 0 or 1 was transmitted.
  • Signal correlator
    • $r_0=\int_0^{T_b}r(t)s_0(t)dt=E+n_0$
    • $r_1=\int_0^{T_b}r(t)s_1(t)dt=n_1$
  • Match filter
    • Sample at $t=T_b$
    • $h_i(t)=s_i(T_{b}-t)，0\leq t \leq T_b，i=0,1$
  • Detector
    • The detector observe the correlator or matched filter output and decides on whether the transmitted signal waveform is either $s_0(t)$ or $s_1(t)$.
    • Comparing $r_0$ or $r_1$. If $r_0>r_1$, it decides 0 is transmitted.
    • $P_e=Q(\frac{E}{\delta})=Q(\frac{E}{\sqrt{\frac{EN_0}{2}}})=Q(\sqrt{\frac{2E}{N_0}})$

• Other binary modulations

  • Antipodal signal
    • $P_e(\alpha_{opt})=Q(\frac{\frac{E}{2}}{\delta})=Q(\frac{\frac{E}{2}}{\sqrt{\frac{EN_0}{2}}})=Q(\sqrt{\frac{E}{2N_0}})$

• Monto Carlo simulation

  • Source output dsource=0ordsource=1
  • Detection
    • Match filter output r=E+gngauss(sgma)orr=-E+gngauss(sgma)
    • Detector r<0? decis=0ordecis=1
    • Error counter decis!=dsource? numoferr+=1

• Constellation diagram

  • $x_1=\sqrt{E}+n_1$
  • $n_1=\sqrt{\frac{N_0}{2}}*randn(100,1)$
  • $x_0=\sqrt{E}+n_0$
  • $n_0=\sqrt{\frac{N_0}{2}}*randn(100,1)$

5.Pulse Amplitude Modulation(PAM)

• Theoretical symbol error rate

  • $P_M=\frac{2(M-1)}{M}Q(\sqrt{\frac{6(log_{2}M)E_{avb}}{(M^2{-}1)N_0}})$
  • SNR=exp(snr_in_dB*log(10)/10)equals toSNR=10^(snr_in_dB/10)

• Bit error rate and energy

  • smld_err_pb=smld_err_p/M

  • Energy(M-PAM)(N symbols)
    $E_{av}=\frac{1}{N}\sum_{k=1}^{N}\int_{0}^{T}s_{k}^{2}(t)dt$
    $E=\frac{E_{av}}{M}$

    %% 8PAM,M=3;there is 8 symbols.
    Eav = sum(Am.^2)/8      % Average energy per symbol.
    E = Eav/3               % Average energy per bit.

• Raised-cosine and ISI

  • RC
    • $x_{rc}(t)=\frac{sin(\pi t/T_s)}{\pi t/T_s}\frac{cos(\alpha \pi t/T_s))}{1-4\alpha ^2 t^2 /T_s^2}$
  • ISI
    • $x(nT)=
      \begin{cases}
      1, & n=0 \\\\
      0, & others
      \end{cases} $
    • F-domain：$\sum_{m=-\infty}^{+\infty}X(f+\frac{m}{T})=T$
  • Under a band-limited noiseless channel, the larger the passband, the smoother the signal.
    • e.g. Band-limited noiseless channel

6.💬Digital transmission via carrier modulation

• Carrier amplitude modulation（ASK）

  • In baseband digital PAM, the signal waveforms are：$s_m(t)=A_{m}g_{T}(t)$
  • $A_{m}=(2m-1-M)d, m=1,2,…,M$
  • Multiplied by a sinusoidal carrier：$u_m(t)=A_{m}g_{T}(t)cos(2\pi f_{c}t)$
  • When the pulse shape is rectangular:$x(nT)=
    \begin{cases}
    \sqrt \frac{2}{T}, & 0\leq t\leq T \\\\
    0, & otherwise
    \end{cases} $
  • Usually called amplitude shift keying, which is not bandlimited.

• Carrier phase modulation(PSK)

  • The information is impressed on the phase of the carrier.
  • The range of the phase：$0\leq \theta \leq 2\pi$
  • $\theta _{m}=\frac{2\pi m}{M}, m=0,1,…,M-1$
  • Modulated signal waveform：$u_m(t)=Ag_{T}(t)cos(2\pi f_{c}t+\frac{2\pi m}{M}), m=0,1,…,M-1$
  • Usually called phase shift keying.
  • e.g. ASK and PSK

• Quadrature amplitude modulation(QAM)

  • Two quadrature carriers,$sin(2\pi f_{c}t)$and$cos(2\pi f_{c}t)$
  • Each is modulated by independent information bits.
  • $u_m(t)=A_{mc}g_{T}(t)cos(2\pi f_{c}t)+A_{ms}g_{T}(t)sin(2\pi f_{c}t), m=0,1,…,M$
  • Carried bits per symbol：$log_{2}M$
  • Can be viewed as a form of combined amplitude and digital-phase modulation.
  • Rewrite：$u_{mn}(t)=A_{m}g_{T}(t)cos(2\pi f_{c}t+\theta _{n}),m=1,2,…,M_1,n=1,2,…,M_2$
  • This time, carried bits per symbol：$log_{2}M_{1}+log_{2}M_{2}$

• Carrier frequency modulation(FSK)

  • For channel lack of phase stability, digital transmission by frequency modulation can be applied.
  • M-ary FSK can be used to transmit a block of $k=log_{2}M$ bits per symbol.
  • $u_m(t)=\sqrt{\frac{2E_s}{T}}cos(2\pi f_{c}t+2\pi m\Delta ft), m=0,1,…,M-1,0\leq t\leq T$
  • To guarantee orthogonality, ∆𝒇 is a multiple of 1/2T.

• Sampling

  • sampling.m

  function [T,Samp_Sig]=Sampling(t,Fs,sig) 
  %Fucntion Name:Sampling 
  %Input: T,Fs:sig OutPut:Samp_Sig 
  %When you call the Function ,u input the time for a symbol,the 
  %Sampling rate and the source signal,then output the Samplint Signal.
  Ts=1/Fs;
  Sig=sig;
  len=length(Sig);
  T=0:Ts:len*t-Ts;
  Samp_Sig=T; 
  for i=0:1:len-1
    for j=1:1:t/Ts
        Samp_Sig(i*t/Ts+j)=Sig(i+1);
    end    
  end

📂Source file.zip