Incorrect values when calculating the optimum value using while loop
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Dear all,
Please I'm trying to get the optimum power allocation Pk using while loop.. but it seems that I wrongly writing this algorithm and all values are equal to 1 without making alot of iterations... please can any one help me to correct the code.. I appreciate your help.
This is my code:
%parameters
TN=10
lamda=[5.3899,2.8511,1.0846,0.4003,2.8364,0.0988,0.0470,0.3417,0.5460,0.5992];
segma_squared=10^-9; %variance
tau=0.1;
Pt=1;
beta= [0.3414,0.1707,0.1138,0.0854,0.0683,0.0569,0.0488,0.0427,0.0379,0.0341];
epsilon=0.001
%%%%%%%%%%%starting of algorithm 1%%%%%%%%%%%%%%%
max_iterations=1000;
power_allocation_initial=[5.3899,4.435,1.1024,0.4004,2.854,0.109988,0.0470,0.3417,0.3450,0.6592];
for i=2:max_iterations
for k=1:TN
gamma_infinity(i,k)=(lamda(k)*(1-tau^2)*power_allocation_initial(k)*Pt)/(segma_squared); % calculation initial value of gamma_inf(0)
w_k(k)=log2(power_allocation_initial(k));
while (gamma_infinity(i,k)-gamma_infinity(i-1,k) >= epsilon)
i=i+1;
gamma_infinity(i,k)=gamma_infinity(i-1,k);
k_(k)=(gamma_infinity(i,k))/(1+gamma_infinity(i,k));
v_k(k)=log2(1+gamma_infinity(i,k))-((gamma_infinity(i,k))/(1+gamma_infinity(i,k)))*log2(gamma_infinity(i,k));
eita_wk(i,k)=-k_(k) *w_k(k)-k_(k)*log2(lamda(k)*(1-tau^2))*Pt + 2*k_(k)*log2(sqrt(segma_squared))-v_k(k);
w(i,k)=beta(k)*eita_wk(i,k);
ww(i)=sum(w(i,k));
[p_opt(k),fval] = linprog(ww(i),[],[],[],[],0,1);
end
end
end
6 个评论
Torsten
2022-11-20
It would be helpful to know what problem (25) is.
bassant tolba
2022-11-20
You do the same mistake as before:
You try to decouple the problem and minimize one single ww(k):
[p_opt(k),fval] = linprog(ww(i),[],[],[],[],0,1);
although you are told to minimize sum(beta_k*gamma(ww(k))
So in short: Delete the loop over k - all values p(k) for k in K (so the complete vector p) have to be determined simulaneously by solving your optimization problem.
bassant tolba
2022-11-20
编辑：bassant tolba
2022-11-20
Dear Torsten,
Thank you really for your reply. I modified the last two lines, but it says that the solution is unbounded and there is no values for p_opt
%parameters
TN=10
lamda=[5.3899,2.8511,1.0846,0.4003,2.8364,0.0988,0.0470,0.3417,0.5460,0.5992];
segma_squared=10^-9; %variance
tau=0.1;
Pt=1;
beta= [0.3414,0.1707,0.1138,0.0854,0.0683,0.0569,0.0488,0.0427,0.0379,0.0341];
epsilon=0.001
%%%%%%%%%%%starting of algorithm 1%%%%%%%%%%%%%%%
max_iterations=1000;
power_allocation_initial=[5.3899,4.435,1.1024,0.4004,2.854,0.109988,0.0470,0.3417,0.3450,0.6592];
for i=2:max_iterations
for k=1:TN
gamma_infinity(i,k)=(lamda(k)*(1-tau^2)*power_allocation_initial(k)*Pt)/(segma_squared); % calculation initial value of gamma_inf(0)
w_k(k)=log2(power_allocation_initial(k));
while (gamma_infinity(i,k)-gamma_infinity(i-1,k) >= epsilon)
i=i+1;
gamma_infinity(i,k)=gamma_infinity(i-1,k);
k_(k)=(gamma_infinity(i,k))/(1+gamma_infinity(i,k));
v_k(k)=log2(1+gamma_infinity(i,k))-((gamma_infinity(i,k))/(1+gamma_infinity(i,k)))*log2(gamma_infinity(i,k));
eita_wk(i,k)=-k_(k) *w_k(k)-k_(k)*log2(lamda(k)*(1-tau^2))*Pt + 2*k_(k)*log2(sqrt(segma_squared))-v_k(k);
w(i,k)=beta(k)*eita_wk(i,k);
end
end
ww=sum(w);
[p_opt,fval] = linprog(ww,[],[],[],[],0,1);
end
Don't you see from the description of the algorithm that the optimization problem has to be solved within the while-loop and not outside ?
And now that you search for a vector p of length 10, your upper and lower bounds also have to be vectors of length 10. But you set them to 0 and 1 - so just one value.
And what is the for-loop good for ? Isn't it a superfluous duplicate of the while-loop ?
bassant tolba
2022-11-20
采纳的回答
With regards to for loop, I used it to be able to do j-1 as a previous step
But the steps (variable i) are done in the while loop - so no further for loop is needed.
I don't understand the underlying problem. Here is a suggestion for code as I understand the algorithm (although problem (25) is quite strange since the extra terms (2nd and 3rd) in the definition of Gamma don't depend on the optimization variable omega):
TN=10;
lamda=[5.3899,2.8511,1.0846,0.4003,2.8364,0.0988,0.0470,0.3417,0.5460,0.5992];
segma_squared=10^-9; %variance
tau=0.1;
Pt=1;
beta= [0.3414,0.1707,0.1138,0.0854,0.0683,0.0569,0.0488,0.0427,0.0379,0.0341];
epsilon=0.001;
power_allocation_initial=[5.3899,4.435,1.1024,0.4004,2.854,0.109988,0.0470,0.3417,0.3450,0.6592];
gamma_infinity_im1=(lamda.*(1-tau^2).*power_allocation_initial*Pt)/(segma_squared); % calculation initial value of gamma_inf(0)
imax = 20;
i = 0;
error = 1;
while error > epsilon && i < imax
i = i+1;
kappa = gamma_infinity_im1./(1+gamma_infinity_im1);
v = log2(1+gamma_infinity_im1)-kappa.*log2(gamma_infinity_im1);
f = -beta.*kappa; % Don't know if there are other terms in gamma(omega) that depend on p except the first
omega = linprog(f,[],[],[],[],-Inf(10,1),log2(Pt)*ones(10,1));
P = 2.^omega
gamma_infinity_i = lamda.*(1-tau^2).*P.'*Pt/segma_squared;
error = sum(abs(gamma_infinity_im1-gamma_infinity_i))
gamma_infinity_im1 = gamma_infinity_i;
end
3 个评论
bassant tolba
2022-11-21
Torsten
2022-11-21
I'm quite sure we both still don't understand the model correctly.
So I suggest you let the MATLAB code pause for a while and read the background article from which the algorithm was developed again carefully.
Do you have a link to the article in question ?
bassant tolba
2022-11-21
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