%% 参数初始化
clear; clc;
I = 2; % 无人机数量
T = 5; % 时间步长
J = 3; % RSU数量
M = 2; % 车辆数量
eta_max = 10; % 最大资源分配
p_max = 100; % 最大功率
Gamma_L = 1; % 低优先级效用系数
Gamma_H = 5; % 高优先级惩罚系数
c = 0.1; % 衰减系数
S = ones(I, T); % 任务大小
D = ones(I, T); % 数据量
tau = ones(I, T); % 阈值
x = rand(I, T); % 无人机固定坐标
y = rand(I, T);
max_iter = 10; % Dinkelbach最大迭代
lambda = 0; % 初始lambda
M_big = 1e3; % 大M法常数
B_mj = 10e6; % 带宽 (Hz)
v = randi([0,1], I, T); % 任务优先级 (0:低, 1:高)
delta = randi([1,3], I, 1); % 无人机本地计算资源 (CPU GHz)
sigma = 0.0009999999999999985; % 噪声功率 (W)
H = 50; % 无人机悬停高度 (m)
% 通信参数（随机生成位置和信道增益）
% 无人机位置 (固定)
x_uav = randi([0,100], I, 1);
y_uav = randi([0,100], I, 1);
% RSU位置 (固定)
x_rsu = randi([0,100], J, 1);
y_rsu = randi([0,100], J, 1);
% 车辆位置 (随机移动)
x_veh = randi([0,100], M, T);
y_veh = randi([0,100], M, T);
P = 0.5;% 视距概率
r_ij = 1;%无人机i和RSUj之间的传输功率
r_im = 1;%无人机i和车辆m之间的传输功率
g_mj = 1;%车辆m和RSU j之间的信道增益
% 初始化变量存储
theta_prev = rand(I, T);
eta_prev = rand(I, J, T);
p_prev = rand(M, I, T);
% Dinkelbach
for iter = 1:max_iter
cvx_begin
variables theta(I, T) eta(I, J, T) p_mi(M, I, T)
variables T_i(I, T) z(I, T)
% 目标函数分段处理
u = 0;
for i = 1:I
for t = 1:T
% 定义T_i(t)的约束
T_l_i(i,t) = (1 - theta(i,t)) * S(i,t) * D(i,t) / delta(i);
% C = theta(i,t) * D(i,t) / (B_mj * rel_entr(1,sigma/(sigma+p_mi(i,t) * g_mj)));
C = theta(i,t) * D(i,t) *inv_pos(B_mj * z(i,t));
T_RSU_i(i,t) = P*theta(i,t)*D(i,t)/r_ij + (1-P) * (theta(i,t)*D(i,t)/r_im + C) + theta(i,t)*S(i,t)*D(i,t) - lambda*eta(i,j,t);
T_i(i,t) = max(T_l_i(i,t),T_RSU_i(i,t));
% Big-M
T_i(i,t) <= tau(i,t) + M_big*(1 - z(i,t));
T_i(i,t) >= tau(i,t) - M_big*z(i,t);
% u_i(t)
u1 = v(i,t)*log(1 + tau(i,t) - T_i(i,t))/log(2) + Gamma_L;
u2 = -v(i,t)*Gamma_H + (1 - v(i,t))*Gamma_L*exp(-c*(T_i(i,t)-tau(i,t)));
u = u + z(i,t)*u1 + (1 - z(i,t))*u2;
end
end
maximize(u)
subject to
% C1-C11
v >= 0; v <= 1;
theta >= 0; theta <= 1;
eta >= 0; eta <= eta_max;
for j = 1:J
for t = 1:T
sum(eta(:, j, t)) <= eta_max;
end
end
p >= 0; p <= p_max;
for m = 1:M
for t = 1:T
sum(p(m, :, t)) <= p_max;
end
end
cvx_end
% lambda
if strcmp(cvx_status, 'Solved')
lambda_new = sum(theta(:) .* S(:) .* D(:)) / sum(eta(:));
if abs(lambda_new - lambda) < 1e-3
break;
end
lambda = lambda_new;
theta_prev = theta;
eta_prev = eta;
p_prev = p;
else
error('CVX求解失败');
end
end
This is the code generated by GPT based on my model, and I am trying to improve it. However,
I cannot run the code and the window said that :
"Error using .*
Disciplined convex programming error:
Cannot perform the operation: {real affine} .* {convex}
Error in * (line 36)
z = feval( oper, x, y );
Error in test_taskoffloading0421 (line 62)
C = theta(i,t) * D(i,t) *inv_pos(B_mj * z(i,t));"
I am sure my model is convex. How could I fix it?(Please ignore other confusing parts in the code.)
