Algorithm 1 Successive Convex Optimization Algorithm

1:Initialize $P_I^r$, $U^r$, $P_E^r$ and $P_k^r$, let $r=1$. 2:repeat 3: Solve problem P2.1 for given $\left\{P_I^r,U^r,P_E^r,P_k^r\right\}$ and obtain the optimal solution as ${\delta }_k^{r+1}$. 4: Solve problem P3 for given $\left\{{\delta }_k^{r+1},P_I^r,U^r,P_E^r,P_k^r\right\}$ and obtain the optimal solution as $P_I^{r+1}$. 5: Solve problem P4.2 for given $\left\{{\delta }_k^{r+1},P_I^{r+1},U^r,P_E^r,P_k^r\right\}$ and obtain the optimal solution as $U^{r+1}$. 6: Solve problem P5.1 for given $\left\{{\delta }_k^{r+1},P_I^{r+1},U^{r+1},P_E^r,P_k^r\right\}$ and obtain the optimal solution as $P_E^{r+1}$. 7: Solve problem P6 for given $\left\{{\delta }_k^{r+1},P_I^{r+1},U^{r+1},P_E^{r+1},P_k^r\right\}$ and obtain the optimal solution as $P_k^{r+1}$. 8:until The fluctuation of the objective value is below a threshold $0\le D_{min}^{BU}\left(r+1\right)-D_{min}^{BU}\left(r\right)\le \epsilon ,\epsilon >0$.