matlab_基于PPP随机几何方法以及蒙特卡洛方法对蜂窝通信通信系统进行仿真

clear; %close all; clc; %这是图14 CASE 1 ASE的程序 % %%%%%%%%%% 程序说明 %%%%%%%%%%%%% % 这是邓娜论文Case 1 的系统仿真程序。 % 这个程序呢，是具有相关性的双层异构蜂窝网络下行仿真，主要考察中断概率。 % 基站分为MBS和PBS，在以MBS为圆心，D为半径的圆内没有PBS的分布，以此表示相关性。 % 用户分为MU和PU，在以MBS为圆心，D为半径的圆内为MU，其余的为PU。 % %%%%%%%%%% 待改进处 %%%%%%%%%%%%% % 1.要不要像于新磊的PPP_Lattic_V3一样，考虑ROI。 % 2.MU和PU用户的区分方法，会较为明显地影响到中断概率的最大最小值，所以如果PU用户选取位于PBS的voronoi区域内的话，会怎么样影响结果？ % %%%%%%%%%% 疑问 %%%%%%%%%%%%%% % 原论文中用户密度和PBS密度相等，当我增大用户密度时发现PU的中断概率增大，减小用户密度时PU的中断概率减小，但在lambda_u大于4.0*10^(-4)时，PU用户的中断概率还是始终大于MU用户。 % 这与我推测的不一样。因为如果用户密度大一些的话，PU用户距离它的服务PBS的平均距离就会减小，距离其它干扰PBS的距离会增大，所以接收信号增大，干扰减小，SINR增大，中断率就会下降。 % 但实际上并非如此。所以为什么？ % %%%%%%%%%% 参数设置问题 %%%%%%%%%% % 当area为1500*1500时，大概需要5~8分钟的计算。此时MBS大概只有40个左右，再考虑到Nb的影响，一个MU受到的干扰基站数不到2个。如果增大area运行时间会急剧增加。 % 需要调和结果准确性与运行时间之间的关系。 %%%%%%%%%%% 拓展 %%%%%%%%%%% % 1.用户除了中断概率之外的其他参数，比如分集增益等（好像只出现在多点协作时？） % 2.基站之间的协作CoMP % 3.两层基站之间没有相关性的仿真 %%%%%%%%%% 对了 %%%%%%%%%% % picture.m用来绘图的。和这个程序是配套的。 %% %泊松分布密度 lambda_m=1.6*10^(-5); lambda_p=1.1*10^(-3); %lambda_p=8.0*10^(-4); %lambda_u=16.0*10^(-4);%lambda_u=8.0*10^(-4); lambda_uu=(0.25:2.5/13:2.5).*10^(-3); %基站发射功率 um=1; up=0.05; rm=40;%macro基站服务距离 rp=10;%pico基站服务距离 alpha=4;%衰减指数 Nb=30;%频道数目，一个基站在一个频道上只能服务一个用户 %D=3*rm*(up/um)^(1/alpha);%macro基站服务半径内没有Pico基站 D=55; sigma2=10^(-11);%噪声功率 W0=10*10^(6);%系统带宽 area=[1500 1500];%撒点区域 ROI=[5000 5000];%研究区域=region of interest cycle_num=5;%大循环次数 sub_cycle_num=20;%子循环次数（fading循环） T_dB=-5:1:20;%SINR门限dB T=5;%5dB ASE_mu=zeros(1,length(lambda_uu)); ASE_pu=zeros(1,length(lambda_uu)); for uu=1:1:length(lambda_uu) lambda_u=lambda_uu(uu); Ps_sta_mu=zeros(cycle_num,1); %行表示每一次撒点对应的不同门限下的中断概率 Ps_sta_pu=zeros(cycle_num,1); for cycle=1:1:cycle_num %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% macro基站撒点 %%%%%%%%%%%%%%%%%%%%%%%%%% num_mbs=poissrnd(lambda_m*area(1)*area(2)); xy_mbs=rand(num_mbs,2)-0.5; xy_mbs(:,1)=xy_mbs(:,1)*area(1); xy_mbs(:,2)=xy_mbs(:,2)*area(2); xy_mbs=xy_mbs(:,1)+xy_mbs(:,2)*1i; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pico基站撒点 %%%%%%%%%%%%%%%%%%%%%%%%%%% num_pbs0=poissrnd(lambda_p*area(1)*area(2)); %pbs0为所有pbs xy_pbs0=rand(num_pbs0,2)-0.5; xy_pbs0(:,1)=xy_pbs0(:,1)*area(1); xy_pbs0(:,2)=xy_pbs0(:,2)*area(2); xy_pbs0=xy_pbs0(:,1)+xy_pbs0(:,2)*1i; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 挖去D内的pico基站 %%%%%%%%%%%%%%%%%%%%%% %dist_pbs_to_all_pbs=zeros(num_pbs0,num_mbs);%所有pbs到mbs的距离矩阵 remain_pbs=ones(1,num_pbs0);%判断是否保留pbs的向量,1为保留，0为去除 for i=1:1:num_pbs0 j=1; while j<=num_mbs dist=abs(xy_pbs0(i)-xy_mbs(j));%计算两点之间的距离 if dist<=D remain_pbs(i)=0;%如果pbs处在某个mbs的D范围内，就要剔除 break;%跳出while循环，考察下一个pbs end j=j+1; end end xy_pbs1=xy_pbs0(find(remain_pbs==1));%剔除mbs范围内的pbs之后剩下的pbs num_pbs1=length(xy_pbs1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%用户撒点%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ue_flag=0; %判断是否需要重新用户撒点的标志 while ue_flag==0 ue_flag=1; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 一次用户撒点 %%%%%%%%%%%%%%%%%%%%%%%% num_ue=poissrnd(lambda_u*area(1)*area(2)); xy_ue=rand(num_ue,2)-0.5; xy_ue(:,1)=xy_ue(:,1)*area(1); xy_ue(:,2)=xy_ue(:,2)*area(2); xy_ue=xy_ue(:,1)+xy_ue(:,2)*1i; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 区分MU和PU用户 %%%%%%%%%%%%%%%%%%%%%%%%% xy_mu=[]; xy_pu=[]; for i=1:1:num_ue j=1; while j<=num_mbs dist=abs(xy_ue(i)-xy_mbs(j)); if dist<=D %%认为小于等与D的由mbs服务。大于D的由pbs服务 (用户距离mbs38.5265时，收到来自mbs和边缘pbs的信号相等) break; end j=j+1; end if j<=num_mbs xy_mu=[xy_mu;xy_ue(i)]; else xy_pu=[xy_pu;xy_ue(i)]; %因为对于PU来说，最后会加一再跳出while循环，所以就变成了num_mbs+1 end end num_mu=length(xy_mu); num_pu=length(xy_pu); %%%%%%%%%%%%%%%%%%%%保证每个基站都有相应的服务用户%%%%%%%%%%%%%%%%%% % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% MU %%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%% 1.mbs服务状况结构体,表征每个mbs的所有可能服务用户%%% mbs_service=struct('num_service_mu',repmat({0},1,num_mbs),'list_service_mu',[]); for i=1:1:num_mu [~,ind]=min(abs(xy_mu(i)-xy_mbs)); %距离该用户最近的mbs mbs_service(ind).num_service_mu=mbs_service(ind).num_service_mu+1; mbs_service(ind).list_service_mu=[mbs_service(ind).list_service_mu i]; end %%%%%%%%%% 2.保证每个mbs都至少有一个服务用户%%%%%%%%%%%%%%%%%%% for i=1:1:num_mbs if mbs_service(i).num_service_mu==0 ue_flag=0; %如果存在mbs没有服务用户，则要求重新进行用户撒点 break; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%% PU %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%% 1.pbs服务状况结构体，表征每个pbs所有可能的服务用户%%%% pbs_service1=struct('num_service_pu1',repmat({0},1,num_pbs1),'list_service_pu1',[]); for i=1:1:num_pu [~,ind]=min(abs(xy_pu(i)-xy_pbs1)); %ind表示距离该PU用户最近的pbs pbs_service1(ind).num_service_pu1=pbs_service1(ind).num_service_pu1+1; pbs_service1(ind).list_service_pu1=[pbs_service1(ind).list_service_pu1 i]; end %%%%% 2.保证每个pbs都有服务用户，没有服务用户的pbs剔除 %%%%% %num_min_p=min(num_pbs1,num_pu); valid_pbs=[]; for i=1:1:num_pbs1 if pbs_service1(i).num_service_pu1~=0 valid_pbs=[valid_pbs i]; end end %pbs_service=pbs_service1(valid_pbs); %所有有效pbs服务结构体 xy_pbs=xy_pbs1(valid_pbs); %剔除所有无服务的pbs之后剩下的pbs num_pbs=length(xy_pbs); %pbs_service2=pbs_service1(valid_pbs); %%%% 3.重新计算剔除之后的所有有效pbs %%%%% pbs_service=struct('num_service_pu',repmat({0},1,num_pbs),'list_service_pu',[]); for i=1:1:num_pu [~,ind]=min(abs(xy_pu(i)-xy_pbs)); %ind表示距离该PU用户最近的pbs pbs_service(ind).num_service_pu=pbs_service(ind).num_service_pu+1; pbs_service(ind).list_service_pu=[pbs_service(ind).list_service_pu i]; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%