Shannon Formula Optimization Problem

Hi, I am trying to use CVX to maximize the sum of transmission rate and I encountered some problems. I know shannon formula: x*log(1+y/x) is jointly concave with x and y. But CVX failed to optimize it and the optimized result worse than the initial result. I am really confused about that and want to seek some help. Here are my codes:


N = 30; %The number of users
h = cell2mat(struct2cell(load(‘path_loss_model.mat’,‘path_loss’)));
h = h(1:30);

B_tot = 20e6; % total bandwidth
p_tot = 40; % dbm
p_tot_w = 10^(p_tot/10)/1e3;

N0 = 10.^(-20.4); % noise spectral density
pn = ones(N,1)*p_tot_w/N;
Bn = ones(N,1)*B_tot/N;
pmin = 1e-3;

h_N0 = h./N0;

r_temp = sum(-rel_entr(Bn,Bn+(pn.*h_N0)));

fprintf(‘total transmission rate: %e\n’, r_temp)
variables p(N) B(N)
maximize sum(-rel_entr(B,B+(p.*h_N0)))
subject to
0 <= p
0 <= B
sum(B) <= B_tot;
sum(p) <= p_tot_w;

r = sum(-rel_entr(B,B+(p.*h_N0)))
fprintf(‘optimized total transmission rate: %e\n’, r)

and the result shows:

Thank you very much!

CVX’s Successive Approximation method was used. It is unreliable, and there are now two better alternatives under CVX.

  1. If you have Mosek available, use that as solver.

  2. Otherwise, follow the directions at CVXQUAD: How to use CVXQUAD's Pade Approximant instead of CVX's unreliable Successive Approximation for GP mode, log, exp, entr, rel_entr, kl_div, log_det, det_rootn, exponential cone. CVXQUAD's Quantum (Matrix) Entropy & Matrix Log related functions . Your program does not need any reformulation in order to use CVXQUAD, because rel_entr is supported.