m基于matlab的光通信的信道估计，均衡，抑制papr误码率仿真,对比ZF,RLS,MMSE三种算法

1.算法描述

可见光通信的信道估计，均衡，抑制papr。

 

不考虑光信道，用传统的无线通信的OFDM的信道估计，均衡，抑制papr  信道估计，均衡最好有两个以上的方法比较

 

%本次仿真载频为2GHz，带宽1MHz，子载波数128个，cp为16

%子载波间隔为7.8125kHz

%一个ofdm符号长度为128us，cp长度为16us

%采用16QAM调制方式

%最大doppler频率为132Hz

%多径信道为5径，功率延迟谱服从负指数分布~exp(-t/trms),trms=(1/4)*cp时长，各径延迟取为delay=[0 2e-6 4e-6 8e-6 12e-6]

 

Zero forcing, ZF：简单，但放大了噪声，性能最差

 

Minimum Mean Square Error, MMSE：考虑了噪声因素，性能比ZF好

 

2.仿真效果预览

matlab2022a仿真如下：

3.MATLAB核心程序 `clc;

clear all;

close all;

warning off;

addpath 'func'

 

 

sel    = 3;

%FFT长度

FFTLen = 64;

%循环前缀长度

CPLen  = 16;

%QAM

M      = 4;

%子载波个数

Ns     = 8;  

w      = ones(FFTLen,1);

SNRdB  = [0:2:26];

 

for ii = 1:length(SNRdB)

    ii

    SNRS = SNRdB(ii);

    NUM  = 0;

    ERR  = 0;

    while ERR <= 2000

          ERR

          NUM          = NUM + 1;

          store_input  = zeros(Ns,FFTLen*M);

          store_output = zeros(Ns,FFTLen*M);

          store_error  = zeros(Ns,FFTLen);

 

          for sym=1:Ns

              %发送数据

              input              = rand(1,FFTLen*M) > 0.5;

              store_input(sym,:) = input;

              %发送

              [signal_tx,input_symbols] = func_transmitter(input,FFTLen,CPLen,M);

              %通过信道

              signal_rx                 = func_channel(signal_tx,SNRS);

              %估计，均衡，

              if sel == 1

                 [signal_recovered,w,error_sym] = func_receiver_mmse(signal_tx,signal_rx,input_symbols,FFTLen,CPLen,M,w);

              end

              if sel == 2

                 [signal_recovered,w,error_sym] = func_receiver_zf(signal_tx,signal_rx,input_symbols,FFTLen,CPLen,M,w);

              end   

              if sel == 3

                  w = zeros(3,1);

                 [signal_recovered,w,error_sym] = func_receiver_rls(signal_tx, signal_rx, FFTLen, CPLen, M, w, 3);

              end                

              

              store_output(sym,:)            = signal_recovered;

              store_error(sym,:)             = error_sym.';

          end

          errors_ext = abs(store_input - store_output);

          errors     = errors_ext(FFTLen+1:length(errors_ext));

          num_errors = sum(sum(errors));

          ERR        = ERR + num_errors;

    end

    BER(ii) = ERR/NUM/(FFTLenM(Ns-1));

end

% figure;

% semilogy(SNRdB,BER,'b-o');

% grid on;

% ylabel('Error');

% xlabel('SNR');

 

if sel == 1

   save rmmse.mat SNRdB BER

end

if sel == 2

   save rzf.mat SNRdB BER

end

if sel == 3

   save rls.mat SNRdB BER

end

 

figure;

load rmmse.mat

semilogy(SNRdB,BER,'b-o');

hold on

load rzf.mat

semilogy(SNRdB,BER,'r-o');

hold on

load rls.mat

semilogy(SNRdB,BER,'k-o');

hold on

legend('MMSE','ZF','RLS');

grid on;

ylabel('Error');

xlabel('SNR');

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