# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 5 Experimental Results and Discussion
### Result Analysis  
To assess its performance fairly, the proposed scheme was compared with four baseline schemes:  
1. Local Computing Only (LCO): the video streams are processed totally at the UEs with \(\sum^{n=1}N_{xm,n}=0\), \(\forall m\in\) M, which has a fixed video frame resolution. 2. Edge Computing Only via OMA (ECO-OMA): the video streams are totally offloaded to and processed at the MEC server with \(x_{m,n}=1\), \(\forall m\in\) M, \(n\in\) N, which has a fixed video frame resolution. 3. JVFRS-TO-RA-DQN via OMA (JVFRS-TO-RA-DQN-OMA): Unlike JVFRS-TO-RA-DQN, task \(D_{m}\) generated by UE \(m\) are offloaded to the MEC server through OMA. Each UE has an independent subchannel. We use \(y_{m}\) to denote whether task \(D_{m}\) offloaded to the MEC server, \(y_{m}=1\) denotes that task \(D_{m}\) were offloaded to MEC sever; otherwise, \(y_{m}=0\). 4. Task offloading and a resource allocation algorithm based on DQN via NOMA (TO-RA-DQN-NOMA) [50]: Compared with JVFRS-TO-RA-DQN, TO-RA-DQN-NOMA does not consider the change in video frame resolution, which means that it has a fixed video frame resolution. 5. Maximum accuracy algorithm via NOMA (MA-NOMA) [46]: Compared with JVFRS-TO-RA-DQN, MA-NOMA implements maximum accuracy with the largest frame resolutions in NOMA. A comparison between the six algorithms is shown in Table 2.  
We first take the experiment in a specific scene with \(k=4\), \(M=10\), \(F=10\) GHz, \(W=12\) MHz and \(\eta_{m}^{min}=200\)\(\times\)\(200\) px. Then, we record the system delay under five \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Number of UEs, \(M\) & 10 \\ Number of NOMA clusters, \(k\) & 4 \\ The distance between the MEC server and UEs & [0, 200] m \\ The total communication bandwidth, \(W\) & 12 MHz \\ CPU cycles required for unit bit task, \(c_{m}^{hit}\) & 100 cycles/bit \\ The computational capacity of the MEC server, \(F\) & 10 GHz \\ The computational capacity of the UEs, \(F_{m}^{loc}\) & [0.4, 2] GHz \\ Average energy consumption threshold, \(E_{m}^{max}\) & 15 J \\ Required bits representing one pixel, \(\tau\) & 24 \\ Maximum transmission power, \(p\) & 0.5 W \\ Maximum tolerance time for task, \(t_{m}^{max}\) & 30 ms \\ Minimum video frame resolution, \(\eta_{m}^{min}\) & 40,000 px (200 \(\times\) 200) \\ Constant of the IoT device, \(v\) & \(1\times 10^{-27}\) \\ Compression ratio of the video frame for UE, \(\rho_{m}\) & 74 \\ Discount factor, \(\tau_{\gamma}\) & [0, 1] \\ Batch size, Z & 128 \\ Replay buffer, \(B\) & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **0–1** & **NOMA** & **Resolution** & **Delay and** \\  & **Offloading** & & **Energy** \\ \hline LCO & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ ECO-OMA & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ JVFRS-TO-RA-DQN-OMA & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ TO-RA-DQN-NOMA & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) \\ MA-NOMA & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) \\ JVFRS-TO-RA-DQN-NOMA & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm comparison. different schemes. We take 1500 experiments and average the experimental data, which is exhibited in Table 3. The average latency of the JVFRS-TO-RA-DQN scheme is 167.71 ms. That is about 48.14% less than the JVFRS-TO-RA-DQN-OMA scheme, about 33.38% less than the TO-RA-DQN-NOMA scheme, and about 59.01% less than the MA-NOMA scheme. The influence of different communication bandwidths on the cost function is shown in Figure 3. We take 1500 experiments and average the experimental data in a scene with \(k=4\), \(M=10\), \(F=10\) GHz, \(\eta_{m}^{min}=200\times 200\) px and \(W\) ranging from 2 MHz to 12 MHz. Figure 3 exhibits the effect of different communication bandwidths on the cost in this scenario under six schemes.