# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 5 Experimental Results and Discussion
### Result Analysis
It is demonstrated that the MA-NOMA algorithm always maintains the highest level of accuracy, which is because the system can detect all objects when the video frame resolution is 700 \(\times\) 700 px. The analytic accuracy of the proposed algorithm is lower than TO-RA-DQN-NOMA's, for the proposed algorithm sacrifices the analytic accuracy to reduce delay and energy consumption. The influence of delay and energy on the system is gradually increasing when the minimum video frame resolution is larger than 500 \(\times\) 500 px, the video analytic accuracy is almost steady. We then depict the process of convergence with the TO-RA-DQN-NOMA algorithm and the proposed algorithm in a scenario with \(k=4\), \(M=10\), \(F=10\) GHz, \(W=12\) MHz and \(\eta_{m}^{min}=200\)\(\times\) 200 px. Figure 7 depicts the performance differences between the TO-RA-DQN-NOMA algorithm and the proposed algorithm at different learning rates. On the one hand, the proposed algorithm obtains correspondingly higher rewards than the TO-RA-DQN-NOMA scheme. This is owing to the fact that different algorithms lead to different offloading decisions, which means that the video frames offloaded to MEC are different, while the average delay and video analysis accuracy are also different. Compared with TO-RA-DQN-NOMA algorithm, the simulation data in Table 4 indicates that the proposed algorithm has better performance than the TO-RA-DQN-NOMA algorithm, in terms of video analytic accuracy. Moreover, the convergence rate of the proposed algorithm is higher than that of the TO-RA-DQN-NOMA algorithm. Furthermore, it was discovered that after training about 200 epochs, the learning rates of the proposed algorithm are \(10^{-6}\) and \(10^{-7}\), converging to a reward value. On the other hand, after training more than 400 epochs, the learning rates of the TO-RA-DQN-NOMA algorithm are \(10^{-6}\) and \(10^{-7}\), converging to a reward value. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **TO-RA-DQN-NOMA** & **Proposed** \\ \hline Learning rate of \(10^{-6}\) & 95.87\% & 98.74\% \\ Learning rate of \(10^{-7}\) & 95.96\% & 98.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The video analytic accuracy of the two schemes. Figure 6: The effect of minimum frame resolution on video analytic accuracy. Figure 8 shows the convergence performance of the proposed algorithm under different numbers of UEs. We take the experiment in a scene with \(k=4\), \(F=10\) GHz, \(W=10\) GHz, \(\eta_{m}^{min}=200\times 200\) px, \(M=10\), \(15\), and \(20\). The algorithm converges rapidly and steadily no matter how many UEs there are. Furthermore, the average reward of 10 UEs apparently exceeds that of 15 UEs. The average reward of 10 UEs converges to a reward for training at approximately 120 epochs, the average reward of 15 UEs converges to a reward for training at approximately 270 epochs, and the average reward of 20 UEs converges to a reward value at over 400 epochs. This is due to the fact that more UEs are able to offload computing tasks at a higher efficiency than when there are fewer UEs, which reduces the energy consumption of UEs while enhancing the users' QoE on the basis of latency, energy consumption, and video analytic accuracy.