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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码、数据、文章下载
💥1 概述
摘要：
下一代物联网（NG-IoT）应用的出现为第六代（6G）移动网络引入了诸多挑战，如大规模连接、增加的网络容量和极低的延迟。为了应对上述挑战，超密集网络已被广泛认为是一种可能的解决方案。然而，基站（BSs）的密集部署并不总是可行或经济高效的。无人机基站（DBSs）可以促进网络扩展并有效应对NG-IoT的要求。此外，由于其灵活性，它们可以在紧急情况下提供按需连接或应对网络流量的临时增加。然而，由于有限的能量储备和空地链路中信号质量降低，DBS的最佳位置的确定并非易事。为此，群体智能方法可能是在三维（3D）空间中确定DBS的最佳位置的吸引人解决方案。在这项工作中，我们探讨了著名的群体智能方法，包括布谷鸟搜索（CS）、大象群体优化（EHO）、灰狼优化（GWO）、帝王蝴蝶优化（MBO）、鲨鱼群算法（SSA）和粒子群优化（PSO），并研究它们在解决上述问题中的性能和效率。具体而言，我们研究了在不同群体智能方法存在的情况下的三个场景的性能。此外，我们进行了非参数统计测试，即弗里德曼和威尔科克森测试，以比较不同的方法。详细文章见第4部分。
📚2 运行结果
部分代码：
%Scenarios 2 and 3
x=[1:10]; %x-axis vector
urban_pathloss(1,:)=CS_avg_pathloss(1,:);
urban_pathloss(2,:)=EHO_avg_pathloss(1,:);
urban_pathloss(3,:)=GWO_avg_pathloss(1,:);
urban_pathloss(4,:)=MBO_avg_pathloss(1,:);
urban_pathloss(5,:)=SSA_avg_pathloss(1,:);
urban_pathloss(6,:)=PSO_avg_pathloss(1,:);
figure
plot(x,urban_pathloss)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Average Pathloss")
title("Average pathloss as a function of the number of DBSs in urban environment")
suburban_pathloss(1,:)=CS_avg_pathloss(2,:);
suburban_pathloss(2,:)=EHO_avg_pathloss(2,:);
suburban_pathloss(3,:)=GWO_avg_pathloss(2,:);
suburban_pathloss(4,:)=MBO_avg_pathloss(2,:);
suburban_pathloss(5,:)=SSA_avg_pathloss(2,:);
suburban_pathloss(6,:)=PSO_avg_pathloss(2,:);
figure
plot(x,suburban_pathloss)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Average Pathloss")
title("Average pathloss as a function of the number of DBSs in suburban environment")
dense_pathloss(1,:)=CS_avg_pathloss(3,:);
dense_pathloss(2,:)=EHO_avg_pathloss(3,:);
dense_pathloss(3,:)=GWO_avg_pathloss(3,:);
dense_pathloss(4,:)=MBO_avg_pathloss(3,:);
dense_pathloss(5,:)=SSA_avg_pathloss(3,:);
dense_pathloss(6,:)=PSO_avg_pathloss(3,:);
figure
plot(x,dense_pathloss)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Average Pathloss")
title("Average pathloss as a function of the number of DBSs in dense-urban environment")
highrise_pathloss(1,:)=CS_avg_pathloss(4,:);
highrise_pathloss(2,:)=EHO_avg_pathloss(4,:);
highrise_pathloss(3,:)=GWO_avg_pathloss(4,:);
highrise_pathloss(4,:)=MBO_avg_pathloss(4,:);
highrise_pathloss(5,:)=SSA_avg_pathloss(4,:);
highrise_pathloss(6,:)=PSO_avg_pathloss(4,:);
figure
plot(x,highrise_pathloss)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Average Pathloss")
title("Average pathloss as a function of the number of DBSs in high-rise urban environment")
% Coverage probability
%Urban environment - 1 , 90dB - 1
urban_coverage(1,:)=CS_avg_coverage(1,1,:);
urban_coverage(2,:)=EHO_avg_coverage(1,1,:);
urban_coverage(3,:)=GWO_avg_coverage(1,1,:);
urban_coverage(4,:)=MBO_avg_coverage(1,1,:);
urban_coverage(5,:)=SSA_avg_coverage(1,1,:);
urban_coverage(6,:)=PSO_avg_coverage(1,1,:);
figure
plot(x,urban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in urban environment (T=90dB)")
%Urban environment - 1 , 100dB - 2
urban_coverage(1,:)=CS_avg_coverage(2,1,:);
urban_coverage(2,:)=EHO_avg_coverage(2,1,:);
urban_coverage(3,:)=GWO_avg_coverage(2,1,:);
urban_coverage(4,:)=MBO_avg_coverage(2,1,:);
urban_coverage(5,:)=SSA_avg_coverage(2,1,:);
urban_coverage(6,:)=PSO_avg_coverage(2,1,:);
figure
plot(x,urban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in urban environment (T=100dB)")
%Urban environment - 1 , 110dB - 3
urban_coverage(1,:)=CS_avg_coverage(3,1,:);
urban_coverage(2,:)=EHO_avg_coverage(3,1,:);
urban_coverage(3,:)=GWO_avg_coverage(3,1,:);
urban_coverage(4,:)=MBO_avg_coverage(3,1,:);
urban_coverage(5,:)=SSA_avg_coverage(3,1,:);
urban_coverage(6,:)=PSO_avg_coverage(3,1,:);
figure
plot(x,urban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in urban environment (T=110dB)")
%Urban environment - 1 , 120dB - 4
urban_coverage(1,:)=CS_avg_coverage(4,1,:);
urban_coverage(2,:)=EHO_avg_coverage(4,1,:);
urban_coverage(3,:)=GWO_avg_coverage(4,1,:);
urban_coverage(4,:)=MBO_avg_coverage(4,1,:);
urban_coverage(5,:)=SSA_avg_coverage(4,1,:);
urban_coverage(6,:)=PSO_avg_coverage(4,1,:);
figure
plot(x,urban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in urban environment (T=120dB)")
%Suburban environment - 1 , 90dB - 1
suburban_coverage(1,:)=CS_avg_coverage(1,2,:);
suburban_coverage(2,:)=EHO_avg_coverage(1,2,:);
suburban_coverage(3,:)=GWO_avg_coverage(1,2,:);
suburban_coverage(4,:)=MBO_avg_coverage(1,2,:);
suburban_coverage(5,:)=SSA_avg_coverage(1,2,:);
suburban_coverage(6,:)=PSO_avg_coverage(1,2,:);
figure
plot(x,suburban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in suburban environment (T=90dB)")
%Suburban environment - 1 , 100dB - 2
suburban_coverage(1,:)=CS_avg_coverage(2,2,:);
suburban_coverage(2,:)=EHO_avg_coverage(2,2,:);
suburban_coverage(3,:)=GWO_avg_coverage(2,2,:);
suburban_coverage(4,:)=MBO_avg_coverage(2,2,:);
suburban_coverage(5,:)=SSA_avg_coverage(2,2,:);
suburban_coverage(6,:)=PSO_avg_coverage(2,2,:);
figure
plot(x,suburban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in suburban environment (T=100dB)")
%Suburban environment - 1 , 110dB - 3
suburban_coverage(1,:)=CS_avg_coverage(3,2,:);
suburban_coverage(2,:)=EHO_avg_coverage(3,2,:);
suburban_coverage(3,:)=GWO_avg_coverage(3,2,:);
suburban_coverage(4,:)=MBO_avg_coverage(3,2,:);
suburban_coverage(5,:)=SSA_avg_coverage(3,2,:);
suburban_coverage(6,:)=PSO_avg_coverage(3,2,:);
figure
plot(x,suburban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in suburban environment (T=110dB)")
%Suburban environment - 1 , 120dB - 4
suburban_coverage(1,:)=CS_avg_coverage(4,2,:);
suburban_coverage(2,:)=EHO_avg_coverage(4,2,:);
suburban_coverage(3,:)=GWO_avg_coverage(4,2,:);
suburban_coverage(4,:)=MBO_avg_coverage(4,2,:);
suburban_coverage(5,:)=SSA_avg_coverage(4,2,:);
suburban_coverage(6,:)=PSO_avg_coverage(4,2,:);
figure
plot(x,suburban_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in suburban environment (T=120dB)")
%Dense urban environment - 1 , 90dB - 1
dense_coverage(1,:)=CS_avg_coverage(1,3,:);
dense_coverage(2,:)=EHO_avg_coverage(1,3,:);
dense_coverage(3,:)=GWO_avg_coverage(1,3,:);
dense_coverage(4,:)=MBO_avg_coverage(1,3,:);
dense_coverage(5,:)=SSA_avg_coverage(1,3,:);
dense_coverage(6,:)=PSO_avg_coverage(1,3,:);
figure
plot(x,dense_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in dense urban environment (T=90dB)")
%Dense urban environment - 1 , 100dB - 2
dense_coverage(1,:)=CS_avg_coverage(2,3,:);
dense_coverage(2,:)=EHO_avg_coverage(2,3,:);
dense_coverage(3,:)=GWO_avg_coverage(2,3,:);
dense_coverage(4,:)=MBO_avg_coverage(2,3,:);
dense_coverage(5,:)=SSA_avg_coverage(2,3,:);
dense_coverage(6,:)=PSO_avg_coverage(2,3,:);
figure
plot(x,dense_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in dense urban environment (T=100dB)")
%Dense urban environment - 1 , 110dB - 3
dense_coverage(1,:)=CS_avg_coverage(3,3,:);
dense_coverage(2,:)=EHO_avg_coverage(3,3,:);
dense_coverage(3,:)=GWO_avg_coverage(3,3,:);
dense_coverage(4,:)=MBO_avg_coverage(3,3,:);
dense_coverage(5,:)=SSA_avg_coverage(3,3,:);
dense_coverage(6,:)=PSO_avg_coverage(3,3,:);
figure
plot(x,dense_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in dense urban environment (T=110dB)")
%Dense urban environment - 1 , 120dB - 4
dense_coverage(1,:)=CS_avg_coverage(4,3,:);
dense_coverage(2,:)=EHO_avg_coverage(4,3,:);
dense_coverage(3,:)=GWO_avg_coverage(4,3,:);
dense_coverage(4,:)=MBO_avg_coverage(4,3,:);
dense_coverage(5,:)=SSA_avg_coverage(4,3,:);
dense_coverage(6,:)=PSO_avg_coverage(4,3,:);
figure
plot(x,dense_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in dense urban environment (T=120dB)")
%High-rise urban environment - 1 , 90dB - 1
highrise_coverage(1,:)=CS_avg_coverage(1,4,:);
highrise_coverage(2,:)=EHO_avg_coverage(1,4,:);
highrise_coverage(3,:)=GWO_avg_coverage(1,4,:);
highrise_coverage(4,:)=MBO_avg_coverage(1,4,:);
highrise_coverage(5,:)=SSA_avg_coverage(1,4,:);
highrise_coverage(6,:)=PSO_avg_coverage(1,4,:);
figure
plot(x,highrise_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in high-rise urban environment (T=90dB)")
%High-rise urban environment - 1 , 100dB - 2
highrise_coverage(1,:)=CS_avg_coverage(2,4,:);
highrise_coverage(2,:)=EHO_avg_coverage(2,4,:);
highrise_coverage(3,:)=GWO_avg_coverage(2,4,:);
highrise_coverage(4,:)=MBO_avg_coverage(2,4,:);
highrise_coverage(5,:)=SSA_avg_coverage(2,4,:);
highrise_coverage(6,:)=PSO_avg_coverage(2,4,:);
figure
plot(x,highrise_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in high-rise urban environment (T=100dB)")
%High-rise urban environment - 1 , 110dB - 3
highrise_coverage(1,:)=CS_avg_coverage(3,4,:);
highrise_coverage(2,:)=EHO_avg_coverage(3,4,:);
highrise_coverage(3,:)=GWO_avg_coverage(3,4,:);
highrise_coverage(4,:)=MBO_avg_coverage(3,4,:);
highrise_coverage(5,:)=SSA_avg_coverage(3,4,:);
highrise_coverage(6,:)=PSO_avg_coverage(3,4,:);
figure
plot(x,highrise_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in high-rise urban environment (T=110dB)")
%High-rise urban environment - 1 , 120dB - 4
highrise_coverage(1,:)=CS_avg_coverage(4,4,:);
highrise_coverage(2,:)=EHO_avg_coverage(4,4,:);
highrise_coverage(3,:)=GWO_avg_coverage(4,4,:);
highrise_coverage(4,:)=MBO_avg_coverage(4,4,:);
highrise_coverage(5,:)=SSA_avg_coverage(4,4,:);
highrise_coverage(6,:)=PSO_avg_coverage(4,4,:);
figure
plot(x,highrise_coverage)
legend("CS", "EHO", "GWO", "MBO", "SSA", "PSO")
xlabel("Number of DBSs")
ylabel("Coverage Probability")
title("Coverage probability as a function of the number of DBSs in high-rise urban environment (T=120dB)")
🎉3 参考文献
文章中一些内容引自网络，会注明出处或引用为参考文献，难免有未尽之处，如有不妥，请随时联系删除。文章来源：https://www.toymoban.com/news/detail-842337.html
OSCAR LIJEN HSU 1 AND CHE-RUNG LEE2文章来源地址https://www.toymoban.com/news/detail-842337.html
🌈4 Matlab代码、数据、文章下载
到了这里，关于基于多种优化算法的物联网无人机基站研究【布谷鸟搜索CS、大象群体优化EHO、灰狼优化GWO、帝王蝴蝶优化MBO、鲨鱼群算法SSA和粒子群优化PSO】（Matlab代码实现）的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
