# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 6 Performance Evaluation
### Simulation Settings  
We consider an 800 m \(\times\) 800 m area that is divided into four 400 m \(\times\) 400 m subareas. Base stations are randomly deployed in the simulation area with a downlink bandwidth of 20 MHz. We set the path loss model as the adapted \(\alpha\beta\gamma\)-model for 5G mobile networks, i.e., \(PL(d)=10\alpha\log_{10}(d)+\beta+10\gamma\log_{10}(f_{c})\), where \((\alpha,\beta,\gamma)=(3.6,7.6,2)\)[17]. Each server has a storage capacity of 300 GB and a computing power of 0.5 GHz. We set the maximum tolerated delay of the users served by each slice to be values randomly selected within \([20,40]\) ms. The computational task size is 100 bits, and the computational demand for each slice is randomly distributed within \([10,30]\) cycles/bit. The propagation delay incurred per unit distance is 0.01 ms/m. The range of \(c_{\alpha}\), \(c_{\gamma}\), \(c_{r}\) and \(c_{c}\) are set to \([20,30]\) USD, \([30,50]\) USD, \([100,150]\) USD/MHz, \([400,500]\) USD/GHz, respectively. Prices are classified into three levels, i.e., \(L=3\). The weights of delay and cost are set as \(w_{d}:w_{c}=1:1\). We use \(U_{max}\) to denote the maximum number of users in the considered area. The number of users served by each slice in each subarea is uniformly distributed in the range \([0,U_{max}/(Q\times N)]\).  
In D3 QN, each neural network is composed of two hidden layers with 512 and 256 units for the shared network module and two hidden layers with 128 units for the state value and action advantage estimators. The BDQs of the NPL and RAL have the same structure, and the shared network modules are the same as that of D3 QN. In BDQ, there are 128 units in each branch of the action advantage estimator as well as the state value estimator. The training parameters are the same for these three layers and are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Number of Neurons in the** \\ **Output Layer of the NPL** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Neurons in the** \\ **Output Layer of the RAL** \\ \end{tabular} &
\begin{tabular}{c} **Computational Complexity** \\ \end{tabular} \\ \hline D3 QN-DBDQ & linear increase & linear increase & linear time complexity \\ \hline Three-layer D3 QN & exponential increase & exponential increase & exponential time complexity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \multicolumn{3}{c}{**System Model Parameters**} \\ \hline Carrier frequency & 2.6 GHz [17] \\ \hline Transmission power & 43 dBm [17] \\ \hline Noise power & \(-174\) dBm/Hz [17] \\ \hline Antenna gain of transmitter and receiver & 15 dBi [17] \\ \hline \multicolumn{3}{c}{**Common Training Parameters for the IPL, NPL and RAL**} \\ \hline Batch size & 64 \\ \hline Discount factor & 0.45 \\ \hline Size of replay buffer & \(10^{6}\) \\ \hline Updating frequency of target network & 500 \\ \hline PER parameter \(\alpha\), \(\beta\) & 0.6, 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.