Algorithm 1 An alternating optimization algorithm based on a feasible point pursuit-successive convex approximation (FPP-SCA)
1. Initialize	Define ${\beta }_i=0.5$, ${\mathbf{F}}_r=\sqrt{\frac{p_r}{Tr\left(\mathbf{R}\right)}}{\mathbf{I}}_{N_R}$, and ${\mathbf{F}}_i=\sqrt{\frac{p_i}{M_T}}{\mathbf{I}}_{M_T}$.
2. Iterative updating	(1) Update ${\mathbf{W}}_i$ using Equation (12) with a fixed ${\mathbf{F}}_i$ and ${\mathbf{F}}_r$.
	(2) Update ${\mathbf{F}}_r$ by solving Equation (14) with a fixed ${\mathbf{F}}_i$ and ${\mathbf{W}}_i$.	a. Set $k=0$ and $vec\left({\mathbf{F}}_r\right)$ as the initial point ${\mathbf{z}}_r^0$. b. Solve Equation (15) at the kth iteration for $k⩾0$ to yield the optimal solution ${\mathbf{f}}_r^k$. c. Let ${\mathbf{z}}_r^{k+1}={\mathbf{f}}_r^k$ and $k=k+1$. d. Until convergence, let ${\mathbf{F}}_r=mat\left({\mathbf{z}}_r^{k+1}\right)$.
	(3) Update ${\mathbf{F}}_i$ by solving Equation (16) with a fixed ${\mathbf{F}}_r$ and ${\mathbf{W}}_i$, following similar steps in (2).
3. Until convergence