基于matlab模拟集群统计MIMO毫米波信道仿真

% This Matlab script can be used to generate Figure 2 (b) in:%% S. Buzzi, C. D'Andrea , "On Clustered Statistical MIMO Millimeter Wave Channel Simulation",% submitted to IEEE Wireless Communications Letters%% In this Figure we show the CDFs of Spectral Efficiency in mmWave% Channel with channel response generate as in expression (1) varying% number of symbols transmit simultaneously on the MIMO channel with Nt=30% and Nr=20 with 0 dBW as value of transmit power, with two different link lengths.%% License: This code is licensed under the GPLv2 License.If you in any way % use this code for research that results in publications, please cite our% original article listed above.clear allclose allclctic%%  Parameter for transmitter and receiver planar arrays Yt=5; % number of transmit antennas on the y-axis of planar arrayZt=6; % number of transmit antennas on the z-axis of planar arrayYr=5; % number of receiver antennas on the y-axis of planar arrayZr=4; % number of receiver antennas on the z-axis of planar arrayNr=Yr*Zr; % number of antennas of the transmit arrayNt=Yt*Zt; % number of antennas of the receive arrayf=73e09; % carrier frequency% Positions of transmitter and receiver in 3-D planeTX_pos=[0 0 7];RX_pos_1=[5 0 1];RX_pos_2=[60 0 1];scenario=2; % UMi-Street Canyon%% Parameters of the filter % We consider RRC pulses as transmitt and receive shaping pulses R=0.22; % roll off factorN=32; % downsampling factorW=500e6;T_symbol=(1+R)/W; % symbol timeTs=T_symbol/N; % sampling time for the filtertt=linspace(-4,4,8*N+1); Tc=T_symbol; % sampling time for the output of the receive filter %% Generation of transmit and receive shaping pulse% We use RRC shaping filters% RRC transmitter shaping filterlen=length(tt);rrc_t=zeros(len,1);for i=1:len   t=tt(i);         if(t==0)       rrc_t(i)= ( 1-R+4*R/pi ) ;   elseif(abs(abs(t)-1/4/R)<1e-3)       rrc_t(i)= (  cos(pi*t*(1-R))*pi*(1-R) + 4*R*cos(pi*t*(1+R)) - 4*R*t*sin(pi*t*(1+R))*(pi*(1+R))  )/(pi)/(1-3*(4*R*t)^2   ) ;      else       rrc_t(i)= ( sin(pi*t*(1-R))+4*R*t*cos(pi*t*(1+R)) ) / (  pi*t*(1- (4*R*t)^2)  );   endend% Normalization of filter as unitary energy filterE_rrc_t=rrc_t'*rrc_t;rrc_t=rrc_t/sqrt(E_rrc_t);% RRC receive shaping filterrrc_r=rrc_t; % Convolution between receiver and transmitter shaping filters and% normalizationh_r_t=conv(rrc_t,rrc_r);E_h=h_r_t'*h_r_t;h_r_t=h_r_t/sqrt(E_h);%% Noise variancenoise_figure=3; % noise figure of the receiver in dBN0=-174;% PSD noise in dBm/Hznoise_variance=W*10^(0.1*noise_figure)*10^(0.1*N0)*10^-3; % F*N0*B%% Calculation of Spectral EfficiencyN_channels=10000;Pt_dB=0;% number of symbols transmitting on the channel simultaneouslyM_1=2; M_2=4;M_3=6;M_4=8;% Initializing variablesSE1_1=zeros(N_channels,1);SE2_1=zeros(N_channels,1);SE3_1=zeros(N_channels,1);SE4_1=zeros(N_channels,1);SE1_2=zeros(N_channels,1);SE2_2=zeros(N_channels,1);SE3_2=zeros(N_channels,1);SE4_2=zeros(N_channels,1);for ch=1:N_channels    % Generation of Channel matrix LTI with RX_pos_1    H_frequency_selective_LTI=Generate_Channel_frequency_selective_LTI(f,TX_pos,RX_pos_1,scenario,Yt,Zt,Yr,Zr,h_r_t,Ts,Tc);    % Spectral Efficiencies    SE1_1(ch,1)= Spectral_efficiency( H_frequency_selective_LTI,Nr,Nt, Pt_dB, M_1 ,noise_variance,rrc_r,N,W,T_symbol);    SE2_1(ch,1)= Spectral_efficiency( H_frequency_selective_LTI,Nr,Nt, Pt_dB, M_2 ,noise_variance,rrc_r,N,W,T_symbol);    SE3_1(ch,1)= Spectral_efficiency( H_frequency_selective_LTI,Nr,Nt, Pt_dB, M_3 ,noise_variance,rrc_r,N,W,T_symbol);    SE4_1(ch,1)= Spectral_efficiency( H_frequency_selective_LTI,Nr,Nt, Pt_dB, M_4 ,noise_variance,rrc_r,N,W,T_symbol);         % Generation of Channel matrix LTI with RX_pos_2    H_frequency_selective_LTI_2=Generate_Channel_frequency_selective_LTI(f,TX_pos,RX_pos_2,scenario,Yt,Zt,Yr,Zr,h_r_t,Ts,Tc);    SE1_2(ch,1)= Spectral_efficiency( H_frequency_selective_LTI_2,Nr,Nt, Pt_dB, M_1 ,noise_variance,rrc_r,N,W,T_symbol);    SE2_2(ch,1)= Spectral_efficiency( H_frequency_selective_LTI_2,Nr,Nt, Pt_dB, M_2 ,noise_variance,rrc_r,N,W,T_symbol);    SE3_2(ch,1)= Spectral_efficiency( H_frequency_selective_LTI_2,Nr,Nt, Pt_dB, M_3 ,noise_variance,rrc_r,N,W,T_symbol);    SE4_2(ch,1)= Spectral_efficiency( H_frequency_selective_LTI_2,Nr,Nt, Pt_dB, M_4 ,noise_variance,rrc_r,N,W,T_symbol);end%% Calculation of Empirical CDFs of Spectral Efficiencies[ x1_1,CDF1_1] = Empirical_CDF(SE1_1);[ x2_1,CDF2_1] = Empirical_CDF(SE2_1);[ x3_1,CDF3_1] = Empirical_CDF(SE3_1);[ x4_1,CDF4_1] = Empirical_CDF(SE4_1);[ x1_2,CDF1_2] = Empirical_CDF(SE1_2);[ x2_2,CDF2_2] = Empirical_CDF(SE2_2);[ x3_2,CDF3_2] = Empirical_CDF(SE3_2);[ x4_2,CDF4_2] = Empirical_CDF(SE4_2);%% Save Resultssave('Results_Figure_2b');%% Figure figureplot(x1_1,CDF1_1,'LineWidth',2);hold onplot(x2_1,CDF2_1,'r','LineWidth',2);hold onplot(x3_1,CDF3_1,'g','LineWidth',2);hold onplot(x4_1,CDF4_1,'k','LineWidth',2);hold onplot(x1_2,CDF1_2,'b--','LineWidth',2);hold onplot(x2_2,CDF2_2,'r--','LineWidth',2);hold onplot(x3_2,CDF3_2,'g--','LineWidth',2);hold onplot(x4_2,CDF4_2,'k--','LineWidth',2);grid onxlabel('Spectral efficiency [bit/s/Hz]','FontSize',12);ylabel('CDF','FontSize',12);AX=legend('M=2,d=10 m','M=4,d=10 m','M=6,d=10 m','M=8,d=10 m','M=2,d=60 m','M=4,d=60 m','M=6,d=60 m','M=8,d=60 m','Location', 'SouthEast');LEG = findobj(AX,'type','text');set(LEG,'FontSize',12)lim_x=max([max(x1_1) max(x2_1) max(x3_1) max(x4_1) max(x1_2) max(x2_2) max(x3_2) max(x4_2)]);xlim([0 lim_x]);toc