## 6 Simulation Results
### Performance Comparison  
In the NOMA system, as the maximum transmission power of the base station is \(p_{\max}=6\)W, the energy efficiency and the sum rate of each algorithm for different number of channels are shown in Figures 6 and 7. It can be seen that the energy efficiency and the \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Complexity** \\ \hline MDRL-UL(Proposed) & \(O\big{(}T_{step}\cdot(N+1)\big{)}\cdot O\big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1} \big{)}\) \\ DRLA & \(O\big{(}T_{step}\cdot M\big{)}\cdot O\big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1} \big{)}+T_{step}\) \\ MDRLA & \(O\big{(}T_{step}\big{)}\cdot O\big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1} \big{)}+T_{step}\) \\ DDPG-UL & \(O(2T_{step})\cdot O\big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1}\big{)}\) \\ DQN-DDPG & \(O\big{(}T_{step}\cdot 2N\big{)}\cdot O\Big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1} \big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complexity of each method. sum rate achieved by the proposed framework of the MDRL-UL are higher than the four other algorithms. This is because the energy efficiency and the transmission rate are not only related to the quality of the channel but also affected by the interference from users in other cells. Each agent of the framework not only considers its own environmental information but also continuously collects its surrounding information through interactions among agents. However, since both MDRLA and DQN-DDPG adopt a distributed architecture where agents operate independently, they cannot guarantee the globally optimal solution. For the other two algorithms, DRLA and DDPG, although they exhibit lower complexity in Table 1, their reliance on a vast action space makes them highly sensitive to even minor deviations, drastically affecting system performance. In contrast, the proposed MDRL-UL method can find better allocation schemes of the channel and the power for the multi-cell NOMA system with lower complexity. Figure 6: The energy efficiency obtained by different algorithms for different number of channels. Figure 7: The sum rate obtained by different algorithms for different number of channels. Let the number of channels be fixed as \(N=4\). As the maximum transmission power of the base station varies from 2 W to 12 W, the sum rate obtained by different algorithms is shown in Figure 8. It can be seen from Figure 8, the sum rate of each algorithm fluctuates at a certain level. According to (3), (4), (5), and (6), as the maximum transmission power of the base station increases, the range of power that can be allocated to each user increases, and the interference from other cells also increases. Therefore, the sum rate of the NOMA system should remain stable. We can observe from Figure 8 that the sum rate obtained by the proposed framework of the MDRL-UL remains at 120 Mbps, with less fluctuation, indicating that the proposed framework of the MDRL-UL has higher stability and performance than other algorithms, which has higher stability and performance than other algorithms. Next, we compare the performance of the algorithms in a time-varying dynamic environment. Considering the time-varying nature of channel gains, the proposed frameworks of the MDRL-UL, the DDPG-UL, and the DQN-DDPG are to find the near-optimal power and channel allocation scheme at each time slot. In addition, the DRLA and the MDRLA are excluded from this comparison as they complete the resource allocation under a specific channel gain while not considering the time-varying nature of the channel gains. For the fairness of the comparison, the proposed frameworks of the MDRL-UL, the DDPG-UL, and the DQN-DDPG are applied to a time-varying dynamic environment with 3 cells, 8 channels, and 12 users. As time slots increase, the energy efficiency and the sum rate of different algorithms are shown in Figures 9 and 10.