基于Matlab模拟时变瑞利衰落信道中的差分放大转发中继

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智能优化算法       神经网络预测       雷达通信      无线传感器        电力系统

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⛄ 内容介绍

1 算法原理

This paper considers the performance of differential amplify-and-forward (D-AF) relaying over time-varying Rayleigh fading channels. Using the auto-regressive time-series model to characterize the time-varying nature of the wireless channels, new weights for the maximum ratio combining (MRC) of the received signals at the destination are proposed. Expression for the pair-wise error probability (PEP) is provided and used to obtain an approximation of the total average bit error probability (BEP). The obtained BEP approximation clearly shows how the system performance depends on the auto-correlation of the direct and the cascaded channels and an irreducible error floor exists at high signal-to-noise ratio (SNR). Simulation results also demonstrate that, for fast-fading channels, the new MRC weights lead to a better performance when compared to the classical combining scheme. Our analysis is verified with simulation results in different fading scenarios.

2 算法流程

Differential Amplify-and-Forward (DAF) relaying is a technique used in wireless communication systems to improve performance in time-varying Rayleigh fading channels. In DAF relaying, the relay node receives the modulated signal from the source node differentially and amplifies it before forwarding it to the destination node.

Here is a general overview of the DAF relaying process in time-varying Rayleigh fading channels:

1. Source-to-relay transmission:
   

• The source node transmits a modulated signal in the first time slot.
  
• The signal experiences fading and attenuation as it propagates through the channel.
  

2. Relay amplification:
   

• The relay node receives the signal differentially, comparing it with a local delayed version of the received signal from the previous time slot.
  
• The relay node amplifies the differentially-encoded signal to enhance its strength.
  

3. Relay-to-destination transmission:
   

• The relay node forwards the amplified signal to the destination node in the next time slot.
  
• The signal undergoes further fading and attenuation during transmission.
  

4. Destination reception:
   

• The destination node receives the relayed signal and demodulates it to recover the transmitted data.
  

Key advantages of DAF relaying in time-varying Rayleigh fading channels include:

1. Diversity gain: The differential encoding at the relay provides diversity gain, allowing for better performance in fading channels.
   
2. Simplified processing: DAF relaying reduces the need for coherent phase and timing synchronization between the source and relay nodes.
   
3. Robustness to fading variations: Differential processing mitigates the impact of channel fading variations over consecutive time slots.
   

It's worth noting that the specific implementation and performance of DAF relaying may vary depending on the system design, modulation scheme, fading characteristics, and other factors. Channel estimation, power control, and cooperation strategies can also be incorporated to further enhance the performance of DAF relaying in time-varying Rayleigh fading channels.

⛄ 部分代码

%% Simulation of the following paper

% M. R. Avendi and Ha H. Nguyen, "Differential Amplify-and-Forward relaying

% in time-varying Rayleigh fading channels," IEEE Wireless Communications

% and Networking Conference (WCNC), Shanghai, China, 2013,

clc

close all

clear all

addpath('functions')

%%

% MPSK modulation

M=2;

Ns=1E5;% number of symbols

Ptot_dB=0:5:40;% SNR scan

Ptot=10.^(Ptot_dB/10);

N0=1; % noise power

% number of Relays

R=1;

% channel distance between channel uses

ch_dis=0; % for symbol-by-symbol n=R

%  scenarios

vfsd=[.001,.01,.05];

vfsr=[.001,.01,.05];

vfrd=[.001,.001,0.01];

% select the scenario

scenario=3;

% normalized Dopplers

fsd=vfsd(scenario);

fsr=vfsr(scenario);

frd=vfrd(scenario);

% auto-correlations

[alfa_sd,alfa]=auto_corr(fsd,fsr,frd,ch_dis);

% power allocation

%[P0,P1]=opt_pow(Ptot,fsd,fsr,frd,M,n);

P0=Ptot./2;

Pi=Ptot./2./R;

Ai2= Pi./(P0+N0);

% CDD power allocation

if M==2, c=.5; else c=.5; end

P0_cdd=c*Ptot;

Pi_cdd=(1-c)*Ptot./R;

Ai2_cdd= Pi_cdd./(P0_cdd+N0);

% this loop scans the SNR range

for ind=1:length(Ptot)

nbits=0;%total number of info sent

err_cdd=0;% error counter

err_tvd=0;% error counter

clc

Ptot_dB(ind)

% this loop keeps going to get a certain amount of errors

ERR_TH=100;

while err_cdd<ERR_TH || err_tvd<ERR_TH

% info bits

xb=bits(log2(M)*Ns);

%binary  to MPSK

v=bin2mpsk(xb,M);

% DPSK modulation

s=diff_encoder(v);

Nd=length(s);

% S-D channel

hsd=flat_cos(Nd,fsd,ch_dis);

% S-R and R-D channels

for k=1:R

hsr(k,:)=flat_cos(Nd,fsr,ch_dis);

hrd(k,:)=flat_cos(Nd,frd,ch_dis);

end

% AWGN noise CN(0,N0)

z_sd=cxn(Nd,N0);

for k=1:R

z_sr(k,:)=cxn(Nd,N0);

z_rd(k,:)=cxn(Nd,N0);

end

%--------------------------------------- received signals

% CDD power allocation

y_sd_cdd=sqrt(P0_cdd(ind))*hsd.*s+z_sd;

for k=1:R

y_sr_cdd(k,:)=sqrt(P0_cdd(ind))*hsr(k,:).*s+z_sr(k,:);

y_rd_cdd(k,:)=sqrt(Ai2_cdd(ind))*hrd(k,:).*y_sr_cdd(k,:)+z_rd(k,:);

end

% propossed power allocation for time-varying

y_sd=sqrt(P0(ind))*hsd.*s+z_sd;

for k=1:R

y_sr_tvd(k,:)=sqrt(P0(ind))*hsr(k,:).*s+z_sr(k,:);

y_rd_tvd(k,:)=sqrt(Ai2(ind))*hrd(k,:).*y_sr_tvd(k,:)+z_rd(k,:);

end

% ---------------------------------------- MRC combining

% classical weights

a0_cdd=1/2;

ai_cdd=1/(1+Ai2_cdd(ind))/2;

vh_cdd=a0_cdd*diff_detector(y_sd_cdd);

for k=1:R

vh_cdd=vh_cdd+ai_cdd*diff_detector(y_rd_cdd(k,:));

end

% propossed weights for time-varying

[at1,at2]=mrc_gains(P0(ind),Ai2(ind),alfa_sd,alfa);

vh_tvd=at1*diff_detector(y_sd);

for k=1:R

vh_tvd=vh_tvd+at2*diff_detector(y_rd_tvd(k,:));

end

% selection combining

%vh2=max((diff_detector(y_sd)),(diff_detector(y_rd)));

% binary detection

bh_cdd=mpsk2bin(vh_cdd,M);

bh_tvd=mpsk2bin(vh_tvd,M);

% error count

err_cdd=err_cdd+sum(abs(xb-bh_cdd));

err_tvd=err_tvd+sum(abs(xb-bh_tvd));

nbits=log2(M)*Ns+nbits;

end

% compute practical BER

BER_cdd(ind)=err_cdd/nbits;% coperative

BER_tvd(ind)=err_tvd/nbits;% coperative

end

% SER

Pu=Pupper(P0,Ai2,fsd,fsr,frd,M,ch_dis);

Ps=Ps_num_int(P0,Ai2,fsd,fsr,frd,M,ch_dis);

Psf=Ps_floor(fsd,fsr,frd,M,ch_dis);

% upper bound

Pb_ub=Pu./log2(M);

% theoretical bit error rate

Pb_theory=Ps./log2(M);

% error floor

Pbf=Psf/log2(M)*ones(1,length(P0));

%% plot

lcm=['b-s';'r-o';'k:>';'g-.';'y-*'];

figure

semilogy(Ptot_dB,BER_cdd,lcm(1,:),'LineWidth',3,'MarkerSize',5);

hold on

semilogy(Ptot_dB,BER_tvd,lcm(2,:),'LineWidth',3,'MarkerSize',5);

%semilogy(Ptot_dB,Pb_ub,lcm(3,:),'LineWidth',3,'MarkerSize',5);

semilogy(Ptot_dB,Pb_theory,lcm(4,:),'LineWidth',3,'MarkerSize',5);

semilogy(Ptot_dB,Pbf,'r-.','LineWidth',3,'MarkerSize',5);

legend('CDD','TVD','Theory','Error Floor')

grid on

xlabel('P(dB)')

ylabel('BER')

⛄ 运行结果

⛄ 参考文献

M. R. Avendi and Ha H. Nguyen, "Differential Amplify-and-Forward relaying in time-varying Rayleigh fading channels," IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, 2013,

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