## 4 Handover Dataset Collection and Deep Learning Model Performance Analysis
### Dataset Collection and Analysis  
Figure 8 shows the scenario being implemented in Mininet-WiFi to collect the training dataset. During the dataset collection, the UE passes through the transmission range of the eNB at a speed of 20 m per second from different tangents and observes the variation of RSRP under different tangents. The collection method is to record the change of RSRP values of UE in every 1 s interval.  
Table 5 shows the features for the training dataset. The training dataset will collect the RSRP values in every one-second interval from the eNB as the input to predict the RSRP values for the next 2, 4, 6, 8, and 10 s. RSRP_t represents the collected RSRP value at time t. The training label of RSRP value after 2 s, i.e., at time t + 2, is RSRP_t + 2. The training label of RSRP value after 4 s is RSRP_t + 4, and so on. Lastly, the training label of RSRP value after 10 s is RSRP_t + 10.  
In this paper, the performance of deep learning models such as MLP, CNN, RNN, and LSTM are compared and the relevant parameters of the models are listed in Table 6.  
* In the MLP model, the numbers of neurons in the first and second layers are both 128, and the number of neurons in the third layer is reduced to 64.
* The CNN model includes a convolutional layer, a pooling layer, and a fully connected layer. The convolutional base layer uses 32 neurons and the fully connected layer uses 16 neurons.  
Figure 7: The relationship between MOS and RSRP for video conference service for UE.  
* The RNN model uses two RNN layers, where the first layer uses 64 neurons and the second layer uses 32 neurons.
* The LSTM model uses two LSTM layers with the same number of neurons as the RNN.  
All the deep learning models are trained using a training dataset with 2840 in number. Each deep learning model is trained for a total of 100 epochs with 50 batch size in each epoch. The performance of each deep learning model is evaluated after training with a testing dataset, which is 525 in number. Some other training parameters are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hidden Layer** & **Neurons** \\ \hline MLP & 3 full connection layers & 128, 128, 64 \\ \hline CNN & 1 convolutional layer, 1 pooling layer, 1 full connection layers & 32, 16 \\ \hline RNN & 2 RNN layer & 64, 32 \\ \hline LSTM & 2 LSTM layer & 64, 32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Deep learning model parameters.  
Figure 8: The UE movement path and RSRP are collected as the training dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Prediction Time** & **Dataset Features** & **Label** \\ \hline Prediction next 2 s of time t & & RSRP\_t + 2 \\ \hline Prediction next 4 s of time t & RSRP\_t, RSRP\_t + 1, RSRP\_t + 2, & RSRP\_t + 4 \\ \hline Prediction next 6 s of time t & RSRP\_t + 3, RSRP\_t + 4, RSRP\_t + 5, & RSRP\_t + 6 \\ \hline Prediction next 8 s of time t & RSRP\_t + 9, RSRP\_t + 10 & RSRP\_t + 8 \\ \hline Prediction next 10 s of time t & & RSRP\_t + 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The features for the training dataset.  
In this study, the measured RSRP values of the UE collected in every second. The RSRP value with the timestamp is used as the training input for the training model to predict the RSRP values. To verify the reliability of the prediction model, we analyzed the results of the deep learning model at different future time instants and predicted the future RSRP value at 2 s intervals for 2, 4, 6, 8, and 10 s.