Algorithm 1 CCCP algorithm for problem (19) for fixed $R_F$

1. Initialize the variables ${\mathbf{P}}^{\left(1\right)}$, ${\Omega }^{\left(1\right)},{\Omega }_0^{\left(1\right)}$ to arbitrary matrices that satisfy the constraints (19b), (19c) and (19d), and set $t\leftarrow 1$. 2. Update the variables ${\mathbf{P}}^{(t+1)},{\Omega }^{(t+1)},{\Omega }_0^{(t+1)}$ as a solution of the convex problem: $$\begin{array}{cc}\hfill \underset{\mathbf{P},\Omega ,{\Omega }_0}{\mathrm{maximize}}& {\tilde{f}}_{\Sigma }\left(R_F,\mathbf{P},\Omega ,{\Omega }_0,{\mathbf{P}}^{\left(t\right)},{\Omega }^{\left(t\right)},{\Omega }_0^{\left(t\right)}\right)\hfill \end{array}$$ (20a) $$\begin{array}{cc}\hfill \mathrm{s}.\mathrm{t}.& R_F\ge {\tilde{g}}_1\left(\Omega ,{\Omega }_0,{\Omega }^{\left(t\right)},{\Omega }_0^{\left(t\right)}\right),\hfill \end{array}$$ (20b) $$\begin{array}{cc}\hfill & 2R_F\ge {\tilde{g}}_{\mathrm{sum}}\left(\Omega ,{\Omega }_0,{\Omega }^{\left(t\right)},{\Omega }_0^{\left(t\right)}\right),\hfill \end{array}$$ (20c) $$\begin{array}{cc}\hfill & \Omega ⪰\mathbf{0},{\Omega }_0⪰\mathbf{0},\hfill \end{array}$$ (20d) $$\begin{array}{c}{P}_1+P_2=P,\hfill \end{array}$$ (20e) $$\begin{array}{c}{P}_1\ge 0,P_2\ge 0.\hfill \end{array}$$ (20f) 3. Stop if a convergence criterion is satisfied. Otherwise, set $t\leftarrow t+1$ and go back to Step 2.