## 4 Proposed Algorithm
### Network Slicing Resource Allocation Algorithm Based on LSTM-D3 QN with DC
Figure 5: The comparison of system throughput for different methods. stage, because our algorithm sacrifices some QoE of the URLLC service and allocates more bandwidth to the eMBB service to obtain more throughput. From the algorithm point of view, even with DQN, the QoE of URLLC can still reach above 0.99 in the later stage, which fully demonstrates the great improvement brought by the DC to the URLLC service. It is worth mentioning that the eMBB service buffer we set can usually be completed in one time slot, which requires the agent to allocate enough bandwidth to eMBB in one time slot to meet the high rate requirements. On the contrary, the buffer of the URLLC service is large and needs to be transmitted in a limited time slot. However, due to the role of the DC, the probability that the URLLC service can be completed within a limited time is greatly increased. Therefore, in our simulation scenario, it is more difficult to meet the requirements of the eMBB service than the URLLC service. This is why DQN can meet the QoE of URLLC in the later stage, but it is difficult to meet the QoE of eMBB. Overall, looking at the overall utility of the system, the throughput and QoE of the eMBB service increase with the number of dual-connected users, showing the impact of our introduction of DC on the slice resource allocation. #### 5.2.2 Simulation Results and Analysis with or without DC Assistance under Different Priorities

Figure 7 shows the comparison of various optimization objectives with and without DC assistance without service priority. From Figure 7a, it can be observed that there are still some points without DC assistance that can reach system utility under DC assistance after convergence. In fact, there is no additional system power consumption without DC assist. Therefore, the utility gap between both situations will be more pronounced if the system energy consumption is not considered. From Figure 7b, we can find that the peak throughput of the system without DC assistance is still about 40 Mb/s less than that with DC assistance after convergence. Figure 7c,d show that the QoE of both eMBB and URLLC can reach 1.0, but the QoE curve is very unstable, and the QoE fluctuation of the URLLC service is larger than that of eMBB, which is even lower than 0.7. Overall, the value of each optimization objective with DC assistance is higher and more stable after convergence than without DC assistance. Figure 6: The comparison of QoE for different methods. (**a**) The comparison of QoE of eMBB; (**b**) the comparison of QoE of URLLC. Figure 8 depicts the comparison of different optimization goals when the eMBB service priority is higher. Figure 8a shows that some strategies can achieve satisfactory values without DC assistance after convergence, and even the values of some points exceed those with DC assistance. From Figure 8b, it can be observed that when the eMBB service priority is higher without DC assistance, the system throughput is stable at about 450 Mb/s after the curve converges, which is 50 Mb/s lower than that with DC assistance. Figure 8c shows that the QoE of eMBB can also be stabilized to 1.0 when the eMBB service priority is higher without DC assistance. We can find from Figure 8d that when the eMBB service has a higher priority without the assistance of the DC, the QoE of the URLLC service after convergence is not as excellent as in the early iteration stage, because the rewards we set are more inclined to satisfy the QoE of eMBB service. In addition, when there is no DC assistance, only the QoE of the URLLC service can be sacrificed to meet the requirements of eMBB. We can also find that the QoE convergence curve trend of URLLC is very similar to the utility curve without DC assistance. This is because the throughput of the system and the QoE of eMBB are very stable after convergence, so the curve trend of QoE of URLLC determines the trend of the utility curve.