🍁🥬🕒摘要🕒🥬🍁
认知无线电技术(CR)是一种解决频谱稀缺问题的新兴技术。CR具有检测、学习环境的能力，并能以最合适的方式智能调整其参数，为用户提供最佳服务。在本文中，我们介绍了一种新的算法叫做蜻蜓算法。该算法用于适应传输参数和优化服务质量。蜻蜓算法(DA)是一种新的基于模拟蜻蜓个体群集行为的元启发式优化算法。该算法是根据蜻蜓的捕猎和迁徙策略而发展起来的。这种捕猎技术被称为静态蜂群(捕食)，在这种方法中，蜂群中的所有成员可以成群结队地飞过一小块区域，寻找食物来源。蜻蜓的迁徙策略称为动态蜂群(迁徙)。在这个阶段，蜻蜓愿意更大规模的群体飞翔，因此，蜂群可以迁移。该算法从最小功耗、误码率、最大吞吐量、最小干扰和最大频谱效率等方面优化QoS性能。将所得结果与另外两种算法遗传算法(GA)和模拟退火(SA)进行了比较。
✨🔎⚡部分运行结果⚡🔎✨
💂♨️👨🎓Matlab代码👨🎓♨️💂
clear all clc SearchAgents_no=200; % Number of search agents dim=5; fobj = @fit_SN; Max_iteration=200; % Maximum number of iterations power_max = 251; %maximum power in 251mW 24dB power_min = 0.158; %minimum power in 0.158mW -8dB bandwidth_max = 32; %maximum bandwidth in Hz bandwidth_min = 2; %minimum bandwidth in Hz tdd_min = 25; %minimum time for transmitting tdd_max = 100; %maximum time for transmitting Mmax = 256; %maximum of modulation index Mmin = 2; %minimum of modulation index Rs_max = 125000; % Maximum symbol rate (Symbols / second) Rs_min = 125; P_Noise_max =3.9810717055e-11 ;%dBm -104 en 3.9810717055e-11mW 6.3095734448e-12 en dBm -112 P_Noise_min = 3.9810717055e-12; %dBm - 114 en 3.9810717055e-12mW 1.995262315e-12 en dBm -117 %Pbe Pbe_max = 0.5768; Pbe_min = 0.2065; Pbe_mean =0.2452; %ub = [power_max, Mmax, bandwidth_max, tdd_max, P_Noise_max, Rs_max]; %lb = [power_min, Mmin, bandwidth_min, tdd_min , P_Noise_max, Rs_min ]; %ub = [power_max, Mmax, bandwidth_max, tdd_max, Pbe_max, Rs_max]; %lb = [power_min, Mmin, bandwidth_min, tdd_min , Pbe_min, Rs_min ]; ub = [power_max, Mmax, bandwidth_max, tdd_max, Rs_max]; lb = [power_min, Mmin, bandwidth_min, tdd_min , Rs_min]; %ub = [power_max, P_Noise_max, bandwidth_max, tdd_max, Rs_max]; %lb = [power_min, P_Noise_min, bandwidth_min, tdd_min , Rs_min ]; %ub = [power_max, Mmax, bandwidth_max, tdd_max]; %lb = [power_min, Mmin, bandwidth_min, tdd_min]; %ub = 166717.99945; %lb = 20817.8311; %ub=[power_max,bandwidth_max,Mmax, tdd_max , P_Noise_max, Rs_max] ; %lb= [power_min,bandwidth_min,Mmin, tdd_min , P_Noise_min, Rs_min] ; %ub=[power_max,power_max,bandwidth_max,bandwidth_max,Mmax,Mmax, tdd_max,tdd_max, P_Noise_max,P_Noise_max,Rs_max, Rs_max] ; %lb= [power_min,power_min,bandwidth_min,bandwidth_min,Mmin,Mmin, tdd_min ,tdd_min , P_Noise_min,P_Noise_min,Rs_min ,Rs_min] ; %Function_name='F20'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) [Best_score,Best_pos,cg_curve]=DA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %figure('Position',[400 400 560 190]) %Draw search space %subplot(1,2,1); %func_plot(Function_name); %title('Test function') %xlabel('x_1'); %ylabel('x_2'); %zlabel([Function_name,'( x_1 , x_2 )']) %grid off %Draw objective space; semilogy(cg_curve,'Color','r','Marker','*') title('Mode Effeciency') xlabel('Iteration'); ylabel('Best score obtained so far'); axis tight grid off box on legend('DA') %a = abs(Best_score); display(['The best solution obtained by DA is : ', num2str(Best_pos')]); display(['The best optimal value of the objective funciton found by DA is : ', num2str(Best_score)]);
📜📢🌈参考文献🌈📢📜
[1]胡静,沈连丰,宋铁成.基于QoS的认知无线电网络MAC协议(英文)[J].Journal of Southeast University(English Edition),2012,28(04):375-379.
