## 4 Proposed Algorithm
### Network Slicing Resource Allocation Algorithm Based on LSTM-D3 QN with DC
The role of the weights here is to make the optimization indicators of different units and orders of magnitude reach the same order of magnitude, so as to facilitate the plotting of the utility function. We adjust the weights to make the utility curve more discriminative, without changing the overall trend. Other parameters in the environment are shown in Table 2. The parameters of LSTM-D3 QN are shown in Table 3. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Bandwidth (MBS/SBS) & 4 MHz, 2 MHz \\ Number of UEs & 700 \\ Type of services & 2 (eMBB and URLLC) \\ Service probability & 1:4 (eMBB : URLLC) \\ Transmitting power & 46 dBm, 30 dBm \\ Radius of cells & 200 m, 50 m \\ Background noise power \(\sigma^{2}\) & \(-\)104 dBm \\ Path loss model (MBS/SBS) & 140 + 40 \(\log(d)\) \\ Additional energy consumption \(\phi\) & 1 \\ QoE: rate (eMBB, URLLC) & 10 ms, 3 ms \\ QoE: latency (eMBB, URLLC) & 100 Mbps, 10 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Total number of iteration T & 8000 \\ Learning rate & 0.001 \\ Discount factor \(\gamma\) & 0.9 \\ Replay memory D & 100,000 \\ Mini-batch & 256 \\ Target network update frequency C & 50 \\ Activation function & Relu \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of LSTM-D3 QN. ### Simulation Results and Discuss

In this part, we first show the simulation results of the proposed algorithm based on LSTM-D3 QN, compared with the algorithms based on DQN and LSTM-A2C. In [12], the resource allocation algorithm based on LSTM-A2C is used for network slicing scenarios and DQN is compared. In particular, the same learning ratios are set for LSTM-D3 QN, LSTM-A2C and DQN. Then, we simulate the impact of resource allocation with and without DC assistance on the optimization metrics from three aspects. Finally, we tested the impact of different numbers of users on the optimization targets of the system. The performance simulation results and analysis are as follows. #### 5.2.1 Simulation Results and Analysis of Different Algorithms

Figure 3 illustrates the change in the system utility for a certain number of iterations. It can be observed that DQN cannot converge to the optimal utility after 8000 iterations, while both our proposed LSTM-D3 QN algorithm and the state-of-the-art LSTM-A2C algorithm can converge to a higher system utility and stabilize at around 7.8 after convergence. Nevertheless, our proposed algorithm is nearly 1500 iterations faster in convergence compared to LSTM-A2C. Figure 4 shows the variation in the number of dual-connected users with the number of iterations. It can be observed that as the number of iterations increases, our proposed LSTM-D3 QN and LSTM-A2C can both converge to 30 users. From Figure 5, we can observe that the throughput has many abnormally high points that are much larger than the convergence value. This situation is due to the fact that the BS allocates the vast majority of bandwidth to one type of slice when dividing the bandwidth, resulting in a very low QoE of another service, even its QoE is equal to 0. Since we give a high weight to the QoE, the overall utility is very low, even though the throughput is abnormally high. It can also be observed that our algorithm has very few outliers and shows better stability in bandwidth allocation. Figure 3: The comparison of system utility for different methods. Figure 6 shows the QoE for eMBB and URLLC services. For QoE of eMBB, it can be observed that both our proposed algorithm and the LSTM-A2C algorithm can converge to about 0.99, while the DQN can only oscillate between 0.6 and 1.0. From the perspective of QoE of URLLC, all three algorithms can reach above 0.99 after training, but we can observe that it stabilizes at 1.0 in the early stage and it drops to about 0.99 in the later stage. Figure 4: The comparison of the number of dual-connected users for different methods.