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Simulation_linear.m
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Simulation_linear.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%this program will run a monte carlo simulation using the FBS Boundary-search algorithm to find the most optimal linear cell edge between the two
%Macro Base Stations based on the net distances of the two FBSs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nodes_i = 7; %number of iterations of node sizes (20 nodes, 30 nodes, 40 nodes ...)
montecarlo_i = 1000; %number of monte carlo instances
nodes = 20; %starting number of nodes
nodes = nodes-10; %initializing node size for the for loop incrementations
seed = 0; %initializing seed which will be used to maintain the distribution of points throughout the instance i of the simulation
net_distance_lin = zeros(nodes_i*montecarlo_i, 7); %initializing matrix which will contain net distance
cell_diameter = 3; %input of distance(km) between the two MBSs
UAV_parameters = [50 60]; %input of [power(W) velocity(m/s)] of UAV
%loop 1 will increment the number of nodes to run montecarlo_i experiments for each number of nodes from 20 to 80 nodes
for node = 1:nodes_i
nodes = nodes + 10; %incrementation of nodes
for i = 1:montecarlo_i
seed = seed + 1; %incrementation of seed so that after every instance i of the monte carlo simulation a new random set of GNs is distributed
rng(seed); %seeding the rand generator
GNs = 0+(cell_diameter-0).*rand(nodes,2); %randomizing nodes GNs ranging from 0 to the cell diameter
%calling function to run FBS_Boundary_search_lin algorithm to find the optimal linear edge at every instance i of the monte carlo simulation
[dnet_lin, ec1_lin, ec2_lin, coordinates_optimal_edge_lin, x_optimal_edge_lin] = FBS_Boundary_search_lin(cell_diameter, GNs, UAV_parameters);
montecarlo_i_nodes_i = i + (montecarlo_i*(node-1)); %variable which will store the total number of runs of the simulation with every iteration
net_distance_lin(montecarlo_i_nodes_i,:) = dnet_lin; %creating matrix containing net distance covered by FBSs at each boundary position for every i for all nodes
end
end
%finding the standard deviation of net distances at each linear boundary of all instances of the monte carlo simulation for every set of nodes
dnet_std_20nodes = std(net_distance_lin(1:montecarlo_i,:));
dnet_std_30nodes = std(net_distance_lin(montecarlo_i+1:2*montecarlo_i,:));
dnet_std_40nodes = std(net_distance_lin(2*montecarlo_i+1:3*montecarlo_i,:));
dnet_std_50nodes = std(net_distance_lin(3*montecarlo_i+1:4*montecarlo_i,:));
dnet_std_60nodes = std(net_distance_lin(4*montecarlo_i+1:5*montecarlo_i,:));
dnet_std_70nodes = std(net_distance_lin(5*montecarlo_i+1:6*montecarlo_i,:));
dnet_std_80nodes = std(net_distance_lin(6*montecarlo_i+1:7*montecarlo_i,:));
%finding the mean net distance at each boundary of all instances of the monte carlo simulation for every set of nodes and the sd of the results
mean_20nodes = mean(net_distance_lin(1:montecarlo_i,:));
results_sd_20 = std(mean_20nodes);
mean_30nodes = mean(net_distance_lin(montecarlo_i+1:2*montecarlo_i,:));
results_sd_30 = std(mean_30nodes);
mean_40nodes = mean(net_distance_lin(2*montecarlo_i+1:3*montecarlo_i,:));
results_sd_40 = std(mean_40nodes);
mean_50nodes = mean(net_distance_lin(3*montecarlo_i+1:4*montecarlo_i,:));
results_sd_50 = std(mean_50nodes);
mean_60nodes = mean(net_distance_lin(4*montecarlo_i+1:5*montecarlo_i,:));
results_sd_60 = std(mean_60nodes);
mean_70nodes = mean(net_distance_lin(5*montecarlo_i+1:6*montecarlo_i,:));
results_sd_70 = std(mean_70nodes);
mean_80nodes = mean(net_distance_lin(6*montecarlo_i+1:7*montecarlo_i,:));
results_sd_80 = std(mean_80nodes);
%bar chart results for every set of nodes containing mean net distance at each boundary and highlighting the optimal boundary
figure(4)
hold on
for bc1 = 1:length(mean_20nodes)
result_20nodes = bar(bc1,mean_20nodes(bc1),'BarWidth',0.5);
if mean_20nodes(bc1) == min(mean_20nodes)
set(result_20nodes,'FaceColor','g');
else
set(result_20nodes,'FaceColor','r');
end
title('20 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(5)
hold on
for bc2 = 1:length(mean_30nodes)
result_30nodes = bar(bc2,mean_30nodes(bc2),'BarWidth',0.5);
if mean_30nodes(bc2) == min(mean_30nodes)
set(result_30nodes,'FaceColor','g');
else
set(result_30nodes,'FaceColor','r');
end
title('30 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(6)
hold on
for bc3 = 1:length(mean_40nodes)
result_40nodes = bar(bc3,mean_40nodes(bc3),'BarWidth',0.5);
if mean_40nodes(bc3) == min(mean_40nodes)
set(result_40nodes,'FaceColor','g');
else
set(result_40nodes,'FaceColor','r');
end
title('40 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(7)
hold on
for bc4 = 1:length(mean_50nodes)
result_50nodes = bar(bc4,mean_50nodes(bc4),'BarWidth',0.5);
if mean_50nodes(bc4) == min(mean_50nodes)
set(result_50nodes,'FaceColor','g');
else
set(result_50nodes,'FaceColor','r');
end
title('50 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(8)
hold on
for bc5 = 1:length(mean_60nodes)
result_60nodes = bar(bc5,mean_60nodes(bc5),'BarWidth',0.5);
if mean_60nodes(bc5) == min(mean_60nodes)
set(result_60nodes,'FaceColor','g');
else
set(result_60nodes,'FaceColor','r');
end
title('60 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(9)
hold on
for bc6 = 1:length(mean_70nodes)
result_70nodes = bar(bc6,mean_70nodes(bc6),'BarWidth',0.5);
if mean_70nodes(bc6) == min(mean_70nodes)
set(result_70nodes,'FaceColor','g');
else
set(result_70nodes,'FaceColor','r');
end
title('70 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off
figure(10)
hold on
for bc7 = 1:length(mean_80nodes)
result_80nodes = bar(bc7,mean_80nodes(bc7),'BarWidth',0.5);
if mean_80nodes(bc7) == min(mean_80nodes)
set(result_80nodes,'FaceColor','g');
else
set(result_80nodes,'FaceColor','r');
end
title('80 Nodes: Optimal Linear Boundary')
xlabel('Boundary x-coordinate (km)')
ylabel('Mean Net-Distance (km)')
set(gca,'xticklabel',{[],'1.05','1.2','1.35','1.5','1.65','1.8','1.95'})
end
hold off