【无人机通信】无线通信网络中无人机UAV定位与带宽分配的优化算法在确保地面用户服务质量QoS约束的同时，最大化网络吞吐量附matlab代码

✅作者简介热爱科研的Matlab仿真开发者擅长毕业设计辅导、数学建模、数据处理、程序设计科研仿真。完整代码获取 定制创新 论文复现点击Matlab科研工作室 关注我领取海量matlab电子书和数学建模资料个人信条做科研博学之、审问之、慎思之、明辨之、笃行之是为博学慎思明辨笃行。 内容介绍一、引言随着无人机UAV在无线通信网络中的应用日益广泛如何在确保地面用户服务质量QoS约束的前提下实现 UAV 的精确定位与合理的带宽分配以最大化网络吞吐量成为了研究的关键问题。高效的定位与带宽分配优化算法不仅能提升 UAV 通信的可靠性与效率还能为地面用户提供更优质的服务体验对于推动 UAV 在通信领域的深入应用具有重要意义。二、问题描述系统模型⛳️ 运行结果 部分代码%% ParamsT 100;delta 0.5;N 200;% altidue 100mH 100;% max velocity, 30m/sv_max 30;% min distance between two UAVs, 50md_min 100;Num_UAV 4;Num_User 10;MAX_X 1000;MAX_Y 1000;%computation parameterc_u 1e3;% local 10s, 20 time-slotsCPUFreq_User 1e9;% CPUFreq_User 5e8;CPUFreq_UAV 10e9;kappa_user 1e-27;kappa_uav 1e-27;%communication parameter (1M Hz, 0.1W)rho 1e-6;Sigma2 1e-14;Pu_max 0.1;Bandwidth 1e6;%constraintE_user_max 2000;E_uav_OE_max 50000;E_uav_prop_max 50000;% % !!! the random seedrng_seed 500;rng(rng_seed);Task_Bit_Vec ones(1,Num_User)*1e7;Loc_User_x rand(1,10)*MAX_XLoc_User_y rand(1,10)*MAX_Y%% hovering Params% Utip 120m/sprop_param_Utip 120;% profile drag coefficientprop_param_delta 0.012;% rho, air density 1.225 kg/m^3prop_param_rho 1.225;% s, rotor solidity, 0.05prop_param_s 0.05;% A, rotor disc areaprop_param_A 0.503;% blade angular velocity in radians/secondprop_param_Omega 300;% Rator radius in mprop_param_R 0.4;prop_param_P0 (prop_param_delta / 8) * prop_param_rho * prop_param_s * prop_param_A * power(prop_param_Omega, 3) * power(prop_param_R, 3);% incremental correction factor to induced powerprop_param_k 0.1;% W, aircraft weight in Newtonprop_param_W 20;prop_param_Pi (1 prop_param_k) * power(prop_param_W, 3 / 2) / sqrt(2 * prop_param_rho * prop_param_A);% d0, fuselage drag ratioprop_param_d0 0.6;% mean rotor induced velocityprop_param_v0 4.03;%% Useful MatrixMatrix_Replicate_10_40 zeros(Num_User, Num_User * Num_UAV);for u1:Num_Userfor m1:Num_UAVMatrix_Replicate_10_40(u, u(m-1)*Num_User) 1;endendMatrix_Replicate_4_40 zeros(Num_UAV, Num_User * Num_UAV);for m1:Num_UAVMatrix_Replicate_4_40(m, (m-1)*Num_User1:m*Num_User) 1;endMatrix_delete_user zeros(Num_User * Num_UAV, Num_User * Num_UAV);for m1:Num_UAVMatrix_delete_user((m-1)*Num_User1:m*Num_User, (m-1)*Num_User1:m*Num_User) 1;endMatrix_delete_user Matrix_delete_user - eye(Num_User * Num_UAV);%% Given ValueGiven_TAU_umn ones(N, Num_User * Num_UAV) / Num_User;Given_L_un ones(N, Num_User) * min(1.5 / N, CPUFreq_User*delta /(Task_Bit_Vec(1)*c_u));Given_P_un ones(N, Num_User).* Pu_max;Given_F_umn ones(N, Num_User * Num_UAV) * CPUFreq_UAV / Num_User;Given_Q_mn_x zeros(N,Num_UAV);Given_Q_mn_y zeros(N,Num_UAV);Given_Qinit_mn_x zeros(1,Num_UAV);Given_Qinit_mn_y zeros(1,Num_UAV);init_center_loc [MAX_X*0.25, MAX_Y*0.75; MAX_X*0.25, MAX_Y*0.25; MAX_X*0.75, MAX_Y*0.75; MAX_X*0.75, MAX_Y*0.25];init_r MAX_X * 0.25;for m1:Num_UAVGiven_Qinit_mn_x(1, m) init_center_loc(m,1) cos(0)*init_r;Given_Qinit_mn_y(1, m) init_center_loc(m,2) sin(0)*init_r;for i1:Ntmp_theta 2 * pi * (i/(N1));Given_Q_mn_x(i, m) init_center_loc(m,1) cos(tmp_theta)*init_r;Given_Q_mn_y(i, m) init_center_loc(m,2) sin(tmp_theta)*init_r;endend 参考文献更多免费数学建模和仿真教程关注领取