## 5 Experimental Results and Analysis
### Simulation Results
#### 5.2.2 DLOO Performance Analysis  
This section details simulations conducted to evaluate the performance of the proposed DLOO framework. The DLOO framework is implemented in Python 3.7.1 with TensorFlow 1.0.0 on a desktop with an Intel Core i5-4590 3.3 GHz CPU and 16 GB of memory. In the proposed DLOO framework, a DBN consisting of one input layer, two hidden layers, and one output layer is employed. The first hidden layer contains 20 neurons, and the second hidden layer comprises 10 neurons. The input to the network is a set of channel coefficients, with the input size varying according to the channel state information. With \(N=6\), \(P_{0}=30\) dBm, and \(\alpha=3\), the number of input neurons is 36, and the output is the UN transmission power, represented by 6 neurons.  
The DLOO framework predicts the transmission power using the DBN trained offline, which could reduce the model complexity under the new channel conditions to solve the optimization problem. First, different UN distribution scenarios are simulated, and the existing model is used to generate training data and further expand the training dataset by solving the problem caused by varying numbers of deployed UNs. Each training sample is obtained by solving the optimization problem using Algorithm 4.  
The experiment simulated a random distribution of six UNs; we use the DLOO framework for power prediction. The experiment is conducted five times, and the learning results are compared with the actual results obtained by the DOPA. As can be seen from Table 2, the error of the DLOO is within 3% higher than the error of the DOPA. The DLOO framework demonstrates the capability to accurately predict each UN's transmission power. Therefore, the DLOO framework could predict each UN's transmission power and link selection in WPMEC, eliminate redundant links, reduce the complexity of the network model and thus, the latency, and satisfy the real-time requirements of edge computing.  
Figure 9 compares the delay of the DLOO with that of the DOPA. With 60 different UN deployments, the DLOO and DOPA calculation times are compared. It can be seen that the calculation time of the DLOO framework was essentially stable at 0.001 s. This delay was within the acceptable range of the UNs, and the calculation time of the DOPA is between 0.5 and 0.6 s. This delay may not be satisfactory for some UNs that are required to handle urgent tasks. The DLOO predicts each UN transmission power value and directly converts the original problem into a linear programming problem. Thus, compared with that of the DOPA, the calculation time is substantially reduced by using the DLOO framework.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Transmission**} & \multicolumn{3}{c}{**Transmission**} & \multicolumn{3}{c}{**Transmission**} \\  & & \multicolumn{3}{c}{**Power of Each**} & \multicolumn{3}{c}{**Power of Each**} & \multicolumn{3}{c}{**Power of Each**} \\  & & \multicolumn{3}{c}{**UN/W**} & & \multicolumn{3}{c}{**UN/W**} & & \multicolumn{3}{c}{**UN/W**} \\ \hline
**UNs** & **Times** & **DOPA** & **DLOO** & **UNs** & **Times** & **DOPA** & **DLOO** & **UNs** & **Times** & **DOPA** & **DLOO** \\ \hline  & 1 & 0.312 & 0.319 & & 1 & 0.872 & 0.896 & & 1 & 0.502 & 0.499 \\  & 2 & 0.918 & 0.907 & & 2 & 0.537 & 0.551 & & 2 & 0.901 & 0.922 \\
0 & 3 & 0.756 & 0.723 & 2 & 3 & 0.39 & 0.412 & 4 & 3 & 0.916 & 0.889 \\  & 4 & 0.502 & 0.513 & & 4 & 0.918 & 0.927 & & 4 & 0.428 & 0.409 \\  & 5 & 0.736 & 0.699 & & 5 & 0.771 & 0.759 & & 5 & 0.056 & 0.102 \\ \hline  & 1 & 0.246 & 0.199 & & 1 & 0.897 & 0.879 & & 1 & 0.781 & 0.782 \\  & 2 & 0.342 & 0.357 & & 2 & 0.921 & 0.924 & & 2 & 0.933 & 0.935 \\
1 & 3 & 0.543 & 0.558 & 3 & 3 & 0.917 & 0.926 & 5 & 3 & 0.502 & 0.519 \\  & 4 & 0.391 & 0.387 & & 4 & 0.791 & 0.788 & & 4 & 0.102 & 0.101 \\  & 5 & 0.685 & 0.671 & & 5 & 0.472 & 0.448 & & 5 & 0.632 & 0.621 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The DLOO framework and the DOPA for power control.