D2D仿真程序

%% This program is to calculate the number of groups in different scenarios % Plot of the number of groups formed with scenario 1, with 250 links and % fixed link lengths, using the first distance model with the greedy % grouping algorithm. clear; clc; % Distance criterium d_min is calculated as follows: n=4; % n is the pass loss exponent % d_D2Dmax=100; % The maximum length of D2D links, in scenario 1, the % value of d_D2Dmax equal to the lengh of links. % fi=6; % fi is the required SINR(dB), in our simulation, the value is % 6dB、9dB or 12dB. for p=1:5 l=0; for L=[1000,500,250,100,50,10] for fi=6:3:12 %% Cell Scenarios With Fixed D2D Link Lengths(500 nodes) Num=250; % Half of Number of nodes R=1000; % Cell Raius(meters) % L=100; % Length of links d_D2Dmax=L; % For fixed D2D link length, d_D2Dmax equal to the length of link L d_min=10.^((fi+10*log10(6))/(10*n))*d_D2Dmax; % Circle,it stands for a cell which has a radiu equal to 1000 meter. seta=0:0.0001:2*pi; x=R*sin(seta); y=R*cos(seta); % figure % plot(x,y,'LineWidth',2,'Color','k') % axis square % hold on % Plot the center of the circle % x_c=0; % y_c=0; % plot(x_c,y_c,'*blue','LineWidth',1.5,'MarkerSize',8) % 5000 Nodes (Part A) Uniformity Distributed in the Cell(均匀分布). r_A=R*sqrt(rand(1,5000)); beta_A=2*pi*rand(1,5000); x_A=r_A.*cos(beta_A); y_A=r_A.*sin(beta_A); % Calculate the other 250 Nodes (Part B) which has a distance 1000 meter with % the Uniformiy Distributed Nodes for i=1:5000 gama_B=2*pi*rand(1); x_B(i)=x_A(i)-L.*cos(gama_B); y_B(i)=y_A(i)-L.*sin(gama_B); Value(i)=x_B(i)+1i*y_B(i); end Find_Node=find(abs(Value)<=1000); Node_A=x_A(Find_Node)+1i*y_A(Find_Node); Node_B=x_B(Find_Node)+1i*y_B(Find_Node); Position_A=Node_A(1:Num); Position_B=Node_B(1:Num); % plot(real(Position_A(1:Num)),imag(Position_A(1:Num)),'+','Color','k') % plot(real(Position_A(1:Num)),imag(Position_A(1:Num)),'o','Color','r','LineWidth',1.5) % hold on % plot(real(Position_B(1:Num)),imag(Position_B(1:Num)),'+','Color','k') % plot(real(Position_B(1:Num)),imag(Position_B(1:Num)),'o','Color','green','LineWidth',1.5) % Connect the two part of the Nodes % for j=1:Num % plot([real(Position_A(j));real(Position_B(j))],[imag(Position_A(j));imag(Position_B(j))],':r') % end %% Compare the distance of the links with d_min % Calculate the minimum distance between every vertice(link). d=[]; d_min_link=[]; vert_degree=[]; % Degree of every vertice(link). for j=1:250 vert_degree(j)=0; for i=j+1:250 d(1)=abs(Position_A(i)-Position_A(j)); d(2)=abs(Position_B(i)-Position_A(j)); d(3)=abs(Position_A(i)-Position_B(j)); d(4)=abs(Position_B(i)-Position_B(j)); % Find the minimum distance d_min_link=min(d); % If the minimum distance between every vertice(link) less than d_min, % degree of every vertice will plus one. if(min(d)<d_min) vert_degree(j)=vert_degree(j)+1; end end end % Sort descending of degree of vertices. [vert_degree_sort,Index]=sort(vert_degree,'descend'); Position_A_sort=Position_A(Index); Position_B_sort=Position_B(Index); %% Use Greedy Grouping Algorthm to ascertain the number of link grouping Position_vert_fulfill=[]; % Matrix put the position value of vertices which fulfill min(d_sort)>d_min Group_matrix_column=2; % The initial value of matrix column of group Group_matrix_row=1; % The nitial value of matrix row of group while (1) k=1; Group_matrix_column=2; M(Group_matrix_row,1)=Position_A_sort(k); N(Group_matrix_row,1)=Position_B_sort(k); if(length(Position_A_sort)<2) break; end for h=k+1:length(Position_A_sort) d_sort(1)=abs(Position_A_sort(h)-Position_A_sort(k)); d_sort(2)=abs(Position_B_sort(h)-Position_A_sort(k)); d_sort(3)=abs(Position_A_sort(h)-Position_B_sort(k)); d_sort(4)=abs(Position_B_sort(h)-Position_B_sort(k)); if(min(d_sort)>d_min) M(Group_matrix_row,Group_matrix_column)=Position_A_sort(h); N(Group_matrix_row,Group_matrix_column)=Position_B_sort(h); Position_vert_fulfill=[Position_vert_fulfill,h]; % Find the position of vertices which fulfill min(d_sort)>d_min Group_matrix_column=Group_matrix_column+1; end end if(length(Position_A_sort)==length(Position_vert_fulfill)+1) break; end Position_A_sort([1,Position_vert_fulfill])=[]; Position_B_sort([1,Position_vert_fulfill])=[]; Position_vert_fulfill=[]; Group_matrix_row=Group_matrix_row+1; end l=l+1; % The number of group M we want to get M_group=Group_matrix_row; Matrix_M_group(p,l)=M_group; % Put all the M_group value to this matrix to plot bar end end end % Find the maximum value and minimum value of M Matrix_M_group_max=max(Matrix_M_group); Matrix_M_group_min=min(Matrix_M_group); % Calculate the average value of M Matrix_M_group_mean=mean(Matrix_M_group); % This three matrix combine to one matrix, so as to plot bar of M/Lt Matrix_M=[Matrix_M_group_min';Matrix_M_group_mean';Matrix_M_group_max']; Final_Matrix_M=reshape(Matrix_M,18,3); %% Plot of the number of groups formed with scenario 1 Lt=250; bar(Final_Matrix_M/Lt); title('由场景1情形下形成的分组结果') grid; legend('Min(M)/Lt','Mean(M)/Lt','Max(M)/Lt') set(gca,'XTickLabel',{'6dB','9dB','12dB','6dB','9dB','12dB','6dB','9dB','12dB','6dB','9dB','12dB','6dB','9dB','12dB','6dB','9dB','12dB'}); xlabel('链路长度（m）'); ylabel('M/Lt') hold on