Algorithm 1 Dinkelbach-assisted Alternating Parameter Optimization

1:Initialize: $\kappa =0$, any feasible $\delta ,\theta ,w$, and $\overline{ϵ}$ : Threshold limit; 2:REPEAT (for a given $\kappa$, iteration: $n)$ 3:  Solve (28)–(32) to obtain $P_{\mathcal{T}}$, for given $\delta ,\theta ,w$; 4:  Utilizing $P_{\mathcal{T}}$, Solve (28)–(32) to obtain $\delta$, for given $\theta ,w$; 5:  Using $P_{\mathcal{T}}$ and $\delta$ , Solve (28)–(32) to obtain $\theta$, for given w; 6:  Finally compute w by solving (28)–(32) via $P_{\mathcal{T}}$, $\delta$, $\theta$; 7: IF (${\omega }_R{R}_U+{\omega }_E{E}_U)-\kappa \left({\omega }_P{P}_{\mathcal{T}}\right)$≤$\overline{ϵ}$ 8:  Convergence = TRUE; 9:  RETURN $\{P_{\mathcal{T}}^{\star },{\delta }^{\star },{\theta }^{\star },w^{\star }\}=\{P_{\mathcal{T}},\delta ,\theta ,w\}$, ${\kappa }^{\star }=\frac{({\omega }_R{R}_U+{\omega }_E{E}_U)}{{\omega }_P{P}_{\mathcal{T}}}$; 10: ELSE 11:   Set $\kappa =\frac{({\omega }_R{R}_U+{\omega }_E{E}_U)}{{\omega }_P{P}_{\mathcal{T}}}$ and $n=n+1$; 12:   Convergence = FALSE; 13: END IF 14:UNTIL Convergence = TRUE.