## 5 Simulation and Performance Analysis  
### Simulation Settings  
In order to verify the performance of the DQN-PIA method, we used Keras [40; 41] as the simulation platform for deep reinforcement learning. Without a loss of generality, we selected the first six groups of CSI data for the performance verification of the proposed method. Due to the spatial differences in CSI information of the wireless devices, these six groups of CSI data could be identified as originating from six different terminals (We believe that the difference in CSI information of the same terminal at different spatio-temporal dimensions is not higher than that of different terminals at different spatio-temporal dimensions. This means that if the proposed method has good discrimination ability for the same terminal at different spatio-temporal dimensions, it also has good discrimination ability for different terminals at different spatio-temporal dimensions). We selected the number of principal components M as six by referring to the threshold of not less than 99% for the sum of the explained variance ratio of the component. The relevant CSI data were trained and learned after the aforementioned preprocessing operations. In this paper, temporal continuous perception data was segmented, with each data size containing 500 samples and a total of 10,000. A total of 80% of the data was used as the training set, while the remaining 20% was set as the test set. The discount factor for Algorithm 1 during operation was set to 0.9, and the exploration factor for the greedy algorithm was set to \(\varepsilon\in[0.9,0.005]\), and \(\varepsilon_{\min}=0.005\) with a decay rate of 0.995. The learning rate was set to 0.01, the size of the experience playback library was set to \(\boldsymbol{\Gamma}=500\), the size of the small batch capacity was set to 32, and the update frequency of the target network was set to \(\phi=500\). The settings of the simulation process's related parameters are summarized in Table 1.  
In order to fully demonstrate the advantages of the DQN-PIA proposed in this paper in terms of terminal authentication performance, the following seven additional types of methods were used for performance comparison: \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Total number of CSI data entries & 10,000 \\ Number of training set entries & 8000 \\ Number of test set entries & 2000 \\ Number of sampling points per entry & 500 \\ Decay rate of \(\varepsilon\), \(\varepsilon_{decay}\) & 0.995 \\ The minimal value of \(\varepsilon\), \(\varepsilon_{\min}\) & 0.005 \\ Experience–Replay memory capacity \(\boldsymbol{\Gamma}\) & 500 \\ Number of Wi-Fi terminals & 6 \\ Update threshold of the target network \(F\) & 500 \\ Experience–Replay minibatch size & 32 \\ Discount factor \(\vartheta\) & 0.9 \\ Learning rate & 0.01 \\ Number of principal components \(M\) & 6 \\ Deep reinforcement learning platform & Keras \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters in the simulation. SVM, the support vector machine method [42], which creates a predictive classification model based on the given features of the dataset elements and uses radial basis function kernels in simulation. * MLP, the multi-layer perceptron method [24], which is a feedforward supervised neural network trained with a standard backward transmission algorithm. In the simulation, the network structure is set as two hidden layers, and the number of units is 15 and 10, respectively. * KNN, the K-nearest neighbor method [20], which determines the type of sample by finding the K-closest samples in the feature space and observing the attribute labels of the latter. We set K = 20. * RF, the random forest method [43], which is a classifier that includes multiple decision trees. The mode of the category output by individual classification trees determines the output category of the method. In this paper, the maximum number of features was set as 15 in the simulation.